Passenger rail station safety improvement and analysis of end-of-track collisions based on systems-theoretic accident modeling and processes (STAMP)

Smart and Resilient Transportation

ISSN : 2632-0487

Article publication date: 4 August 2021

Issue publication date: 26 August 2021

At the US passenger stations, train operations approaching terminating tracks rely on the engineer’s compliant behavior to safely stop before the end of the tracks. Noncompliance actions from the disengaged or inattentive engineers would result in hazards to train passengers, train crewmembers and bystanders at passenger stations. Over the past decade, a series of end-of-track collisions occurred at passenger stations with substantial property damage and casualties. This study’s developed systemic model and discussions present policymakers, railway practitioners and academic researchers with a flexible approach for qualitatively assessing railroad safety.


To achieve a system-based, micro-level analysis of end-of-track accidents and eventually promote the safety level of passenger stations, the systems-theoretic accident modeling and processes (STAMP), as a practical systematic accident model widely used in the complex systems, is developed in view of environmental factors, human errors, organizational factors and mechanical failures in this complex socio-technical system.

The developed STAMP accident model and analytical results qualitatively provide an explicit understanding of the system hazards, constraints and hierarchical control structure of train operations on terminating tracks in the US passenger stations. Furthermore, the safety recommendations and practical options related to obstructive sleep apnea screening, positive train control-based collision avoidance mechanisms, robust system safety program plans and bumping posts are proposed and evaluated using the STAMP approach.


The findings from STAMP-based analysis can serve as valid references for policymakers, government accident investigators, railway practitioners and academic researchers. Ultimately, they can contribute to establishing effective emergent measures for train operations at passenger stations and promote the level of safety necessary to protect the public. The STAMP approach could be adapted to analyze various other rail safety systems that aim to ultimately improve the safety level of railroad systems.

  • Passenger station
  • Positive train control
  • Systematic accident modeling
  • Train accident

Zhang, Z. , Liu, X. and Hu, H. (2021), "Passenger rail station safety improvement and analysis of end-of-track collisions based on systems-theoretic accident modeling and processes (STAMP)", Smart and Resilient Transportation , Vol. 3 No. 2, pp. 94-117.

Emerald Publishing Limited

Copyright © 2021, Zhipeng Zhang, Xiang Liu and Hao Hu.

Published in Smart and Resilient Transportation . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at

1. Introduction

A train approaching the end of terminating tracks at passenger stations is one common train operation scenario in the USA. Passenger stations normally involve multiple platforms and a crowded public area. For example, the Hoboken Terminal in New Jersey contains 17 passenger platform tracks, among which New Jersey Transit (NJT) and the Port Authority Trans-Hudson provided around 15,600 and 30,800 passenger ridership per weekday on average, in 2017 ( NJ Transit, 2018 ; Port Authority Trans-Hudson, 2018 ). At the US passenger stations, there is currently no mechanism implemented that can automatically stop a train before the end of terminating tracks and prevent trains from end-of-track collisions. In other words, when trains are entering passenger stations with stub-end tracks, the engineers’ behavior will determine whether they can safely stop before the end of terminating tracks in general. With this type of train operation at passenger stations, passengers, crewmembers and bystanders are sometimes exposed to hazards resulted from noncompliant train operation if an engineer is disengaged or inattentive. Human errors occurred from time to time, and a series of end-of-track collisions took place at American passenger stations in the past decade. For example, two end-of-track collisions happening at a terminal station of Hoboken Terminal, New Jersey on September 29, 2016 and Atlantic Terminal, Brooklyn, New York on January 4, 2017, have gained concerns from the public and the rail industry. Both of them occurred because of an engineer’s failure to stop the train before it reached the end of the track, each of which resulted in over 100 casualties. The National Transportation Safety Board (NTSB) (2018a) claimed that the safety issues identified from these two accidents also existed throughout the USA at many intercity passenger and commuter passenger train terminals. Despite this ubiquitous risk and an increasing concern on this specific train operation, to our knowledge, prior research studying on end-of-track collisions at passenger stations is quite limited. The development of this paper is motivated by this knowledge gap, in which end-of-track collisions at American passenger stations are studied through a system-based and micro-level risk analysis. The NJT train accident at Hoboken Terminal in 2016 leading to severe consequences to masses is selected as a case study on train operation at passenger stations.

To achieve an explicit understanding of end-of-track collisions and eventually improve the safety of passenger stations, a systematic accident model called Systems-Theoretic Accident Modeling and Processes (STAMP) is used with reference information based on accident investigation results released by NTSB (2018a , 2018b , 2018c ) and FRA (2018) . The safety of STAMP envisions, as a control problem embedded in an adaptive socio-technical system and accidents, is caused by an inadequate control or the violation of safety-related constraints resulted from component failures, external disturbances or dysfunctional interactions among system components (e.g. human factors, physical system and environment) ( Leveson, 2003 , 2004 ). This accident model has been widely employed in diverse domains, including rails ( Ouyang et al. , 2010 ; Song et al. , 2012 ; Underwood and Waterson, 2014 ), aircrafts and spacecrafts ( Ishimatsu et al. , 2014 ; Allison et al. , 2017 ) as well as gas industries ( Altabbakh et al. , 2014 ), which can contribute to a safer system to prevent accidents effectively ( Leveson, 2003 ). The STAMP-based analytical results in this paper provide an explicit safety analysis of physical components, human errors, environmental factors and their interrelationship in the complex terminal operating system, which discloses the inadequate safety constraints at each hierarchical level of end-of-track collisions and contributes to the establishment of safety recommendations as well as suggestions. In addition to the contributions to this specific strategy for train accident risk mitigation and prevention, as the first system-based study on the American railroad industry based on STAMP, it can also be a practical investigation methodology for governmental accident investigators, railway practitioners and academic researchers. Although previous researchers have conducted STAMP-based studies on railways in China ( Ouyang et al. , 2010 ; Song et al. , 2012 ) and the UK ( Underwood and Waterson, 2014 ), different countries would have different hierarchical levels from the role of legislatures, federal agencies to crewmembers. For example, different American railroads may have different operational characteristics, while Chinese railways are managed and controlled primarily by the Government on a consolidated basis ( Beck et al. , 2013 ).

The rest of this paper is organized as follows. First, Section 2 gives a brief overview of common accident analysis methods with a summarized comparison based on previous studies. Section 3 introduces the end-of-track collision at passenger stations, as well as the knowledge gap that motivated the development of this study. In Section 4, STAMP, as the methodology in the paper, is presented with its basic structure and the basic usage in end-of-track collisions at passenger stations. Based on the developed general STAMP model, one selected accident is studied in Section 5, and safety promotion strategies are discussed in Section 6. Finally, this paper concludes with major analytical results and safety findings.

2. Relevant prior literature with respect to accident models

Appropriate accident models perform the foundation of accident investigation and prevention strategies. The common accident analysis methods can be classified into several major categories, including but not limited to the Swiss Cheese model (SCM) and SCM-based models; sequential models; and systematic models. SCM was developed by Reason (1990) and proposed that adverse events result from a series of contributing flaws (like the holes in the cheese slices) that must be aligned. The human factors analysis and classification system (HFACS), the Australian Transport Safety Bureau (ATSB) and EUROCONTROL are universal accident analysis approaches inspired by SCM. Sequential models include fault tree analysis (FTA), event tree analysis (ETA) and failure mode and effect analysis (FMEA), most of which are classic techniques for reliability engineering over the past few decades. Moreover, AcciMap, the functional resonance accident model (FRAM), the driver reliability and error analysis model (DREAM) and STAMP are prevailing systematic models. Selected accident models from these three major categories are extensively studied in prior literature ( Table 1 ).

In comparison with SCM-based models and sequential models, systematic models have better performances in the accidents from the complex systems, such as rail system ( Ouyang et al. , 2010 ; Song et al. , 2012 ; Underwood and Waterson, 2014 ) and aviation ( Ishimatsu et al. , 2014 ; Allison et al. , 2017 ). Previous researchers ( Leveson, 2012 ; Hollnagel, 2012 ) who have drawn some criticisms on the SCM-based models pointed out that SCM-based models oversimplify accident causation through a linear chain of events. In complex systems, non-linear interactions among environmental factors, human errors, organizational factors and mechanical failures may get involved and cannot be described comprehensively using these traditional models. However, systematic models, such as AcciMap and STAMP, are developed to seek to overcome these limitations of complex relationships and provide an explicit understanding of sophisticated accident causations. Ferjencik (2011) pointed out that systematic models are able to offer a deeper judgment and insight into the hazards and risks from dynamic processes and complex systems. Sequential models also have a similar weakness comparing to systematic models ( Al-shanini et al. , 2014 ). Although there is a large number of conjunctive conditions and contributors in some adverse events, sequential models typically describe accidents as certain combinations of failures or events. Al-shanini et al. (2014) argued that sequential models cannot represent multi-linear causes or nonlinear causes in accidents. Therefore, systematic models are applicable in the analysis of the end-of-track collisions at passenger stations, as a system involving multiple system components with complicated interactions.

To our knowledge, there is no direct comparison between STAMP and all other non-STAMP systematic models, but Underwood and Waterson (2014) compared STAMP against AcciMap and concluded that STAMP provides more explicit descriptions of system structure, component relationships, and system behavior and that STAMP may be a more appropriate option for researchers with some features, such as greater thoroughness and taxonomy. With these features, in the domain of rail safety and train accident study, Ouyang et al. (2010) and Underwood and Waterson (2014) have implemented a STAMP-based analysis on the Jiaoji railway accident in China and the Grayigg train derailment in the UK, respectively. As a systemic accident analysis method that can embody the concepts of systems theory, STAMP is selected in this paper to study the end-of-track collisions at passenger stations in the USA. The rail safety operation constraints, hierarchical levels of control and process models of the STAMP model developed in this paper can also be adapted to the studies of other train accidents in the nationwide US railway system.

3. End-of-track collisions at passenger stations

There are at least 35 passenger stations with multiple tracks that end at a bumping post and/or platform in the USA ( NTSB, 2018a ). Bumping post is a safety device placed at the end of terminating track to stop unauthorized movement and can provide limited protection for low impacts. Passenger stations commonly comprise multiple platforms and crowded people that are exposed to potential hazards resulting from noncompliant train operations. For example, New York Penn Station is the busiest passenger transportation facility in the USA and involves 21 tracks and 11 island platforms. It has a ridership of over 300,000 on the average weekday in 2016, among which LIRR contributes to around a ridership of 233,000 ( LIRR, 2017 ). As major transportation hubs in the New York metropolitan area, the Hoboken Terminal has 17 passenger tracks and Newark Penn Station has eight tracks, in which NJ Transit provided around 15,600 passenger boardings and 28,000 passenger boardings, respectively, per weekday in 2017 ( NJ Transit, 2018 ).

In the USA, trains approaching terminating tracks are required to operate at restricted speeds, which are defined as a speed that permits stopping within one-half the range of vision, but not exceeding 20 miles per hour ( FRA, 2011 ). On one side, “stop within one-half the range of vision” could be challenging, especially under adverse environmental conditions (e.g. foggy) or complex terrain characteristics (e.g. descending grade). On the other side, in current station operations, stopping a train on a terminating track usually relies on the attentiveness and compliance of the train crews. Under the circumstances, violation of restricted speed rules at passenger stations is one common type of rule compliance problem on US railroads with potentially high consequences. Human errors occurred now and then and a series of end-of-track collisions at passenger stations happened in the past decade. For example, LIRR trains caused 15 collisions with bumping posts at passenger stations in New York between 1996 and 2010, and NJT also reported seven end-of-track collision accidents in the last ten years ( NTSB, 2018a ). Most recently, two accidents, NJT train collision at Hoboken Terminal in 2016 and LIRR train collision at Atlantic Terminal in 2017, led to over 100 casualties and millions of damage cost each and both of them were end-of-track collisions at passenger stations that were operating at restricted speeds. Despite the serious risk and the increasing concerns, few literatures have conducted studies on end-of-track collisions at US passenger stations. To narrow the knowledge gap, a system-based risk analysis on end-of-track collisions is essential to increase the safety level at passenger stations.

4. Stamp-based accident analysis of end-of-track accidents at passenger stations

4.1 structure of systems-theoretic accident modeling and processes-based accident analysis.

In STAMP models, safety (e.g. train operation safety at stub-end passenger stations) is viewed as a control problem. Leveson (2003) summarized that accidents took place owing to an inadequate enforcement of safety-related constraints on the development, design and operation of the system instead of a series of failure events. Three basic concepts in STAMP, namely, the hierarchical level of control, constraints and process models, are briefly introduced in the following.

In system theory, systems are viewed as hierarchical structures, in which each hierarchical level imposes constraints on the activity of the level below it. Constraints or the lack of constraints at a certain level would control or permit lower-level behavior ( Checkland, 1981 ), which includes the engineering design, physical components, management, human factors and regulatory behavior. Components that violate safety-related constraints of the system or their interactions are likely to result in accidents. Taking train operation in the USA as an example, a hierarchical socio-technical control structure combines five socio-technical system levels, namely, the American Congress, governmental agencies (e.g. Federal Railroad Administration (FRA), NTSB), industrial associations (e.g. American Public Transportation Association and Association of American Railroads), railroad companies and operating processes involving train crewmembers as well as train movements from top to bottom in general.

Apart from constraints and hierarchical levels of control, the process model is also a basic concept in STAMP. Figure 1 shows a basic process control loop where a human controller (e.g. a train engineer) takes charge of train operation. In essence, there are two common controllers in the model of a controlled system, namely, the human controller and the automated controller. Based on commonly employed train operation methods in the USA, train movements are primarily controlled and managed by human controllers, which are also supervised by a train protection controller, such as positive train control (PTC). PTC is a train control system capable of a reliable and functional prevention of train accidents attributable to human errors by slowing down or stopping trains automatically. It is indicated that the PTC system is not a completely automated controller everywhere, which, instead, functions and takes charge of train operation only if the human controller (e.g. train engineer) fails to or inadequately controls the train safely and properly, even though PTC keeps monitoring the performance of engineers and train movements. Therefore, in Figure 1 , the interconnection between the train control system and the actuator (commands applied by train control system to actuator) is marked with a dashed line, representing that this channel works conditionally and is not always active. Furthermore, since the mandate of the Rail Safety Improvement Act in 2008 ( Congress of the United States of America, 2008 ), a nationwide implementation of PTC has been underway in the USA. Railroads serving for toxic- or poisonous-by-inhalation hazardous materials and those providing a regular intercity or commuter rail passenger transportation were required to implement the PTC system by December 31, 2018, with the opportunity for an additional two years upon the approval from FRA ( FRA, 2011 ; Congress of the United States of America, 2015a ). It means that American railroads are currently in the deploying and implementing process of the PTC systems, such as the Interoperable Electronic Train Management System used by Class I freight railroads and the Advanced Civil Speed Enforcement System (ACSES) used by the National Railroad Passenger Corporation (Amtrak) on the Northeast Corridor. Furthermore, the concept of several terms (e.g. sensor, actuator, disturbance, process input and process output) in the process model are also interpreted through explanatory descriptions with common examples in Table 2 .

4.2 Systems-theoretic accident modeling and processes in end-of-track collisions at passenger stations

This section applies the STAMP model to study end-of-track collisions with potential high consequences at passenger stations. Figure 2 shows the general safety control structure of train operations and major safety-related requirements at passenger stations. The general system hazard related to the train operations at the stub-end passenger terminals is the failure of the train to stop at the end of the terminating track and to collide with the bumping post. This hazard should be prevented with system safety constraints, as shown in Figure 2 . These general constraints must be enforced by the entire socio-technical control structure at passenger stations to achieve a positive stop before reaching bumping posts. In other words, end-of-track collision at passenger stations results from either lack of or inadequate enforcement of the constraints at a certain hierarchical level. All the hierarchical levels and controllers are interpreted with brief discussions as follows. To clarify, numerous federal agencies and rail industry associations are related to the train operation safety in the USA, while this study only considers some of them that have a close relationship with train operations at passenger stations.

The US Congress, as the bicameral legislature of the federal government, vests all legislative powers. New laws and changes in existing laws can only be enacted with the consent of the US Congress.

FRA is an agency in the US Department of Transportation (USDOT) with the mission to facilitate the safe and reliable movement of both passenger and freight in the USA by way of establishment and enactment of safety regulations, promotion of rail infrastructure and services, data-driven analysis and development of emerging technologies and innovative solutions in support of rail safety and operational performance ( USDOT, 2017 ).

FTA is also an agency within USDOT. It provides financial and technical assistance to public transit, including light rail, subways, buses ( FTA, 2018 ). FTA receives funding authorized by US Congress in transportation legislation, such as the Fixing America’s Surface Transportation Act ( Congress of the United States of America, 2015b ).

National Institutes of Health (NIH) is an agency of the US Department of Health and Human Services. NIH publishes and supports foremost medical research studies and some of them could guide the physical examination in railroads. For example, an NIH study of interactions between obesity and obstructive sleep apnea (OSA) ( Romero-Corral et al. , 2010 ) found out that a body mass index of more than 35 kg/m 2 indicates severe obesity and increased risk of OSA.

NTSB is an independent US government investigative agency that is responsible for transportation accident investigations on aviation, highway, marine, pipeline and railroad modes. Although NTSB has no formal authority to regulate or be directly involved in the operation of transportation, it provides objective viewpoints through conducting independent investigations and making well-considered recommendations to improve transportation safety ( NTSB, 2018d ).

Railroad industry associations are the industry groups that represent specific modes of rail transportation. For example, the American Public Transportation Association (APTA) is a nonprofit organization that represents all modes of public transportation (e.g. commuter rail, light rail, subways) in the USA. To achieve safe and economical public transportation services and support the growth of federal investments and resource advocacy, APTA provides manuals for transportation modes, education for the public and training programs for workforce, and lobbies to the US Congress ( APTA, 2018 ). The Association of American Railroads (AAR) primarily represent the seven Class I freight railroads (a group of the largest railroads operating in the USA with each railroad annual operating revenue over US$447.6m in 2016) of North American, Amtrak, some non-Class I, and regional commuter railroads, and rail suppliers ( AAR, 2018 ). Some short line and regional railroads are also represented by the American Short Line and Regional Railroad Association. The American Railway Engineering and Maintenance-of-Way Association is an industry group that develops technical manuals and recommended practices for the railway infrastructure, including the process from design, construction, to maintenance ( American Railway Engineering and Maintenance-of-Way Association, 2018 ).

Railroads in the USA play a key role in the national economy, with both freight shipment and passenger service. In terms of passenger railroads, Amtrak is the largest intercity passenger railroad that provides national passengers a rail network connecting over 500 destinations in 46 states, as well as three Canadian provinces ( Amtrak, 2017 ). In addition, there is also a list of commuter rails existing in some metropolitan areas, such as Long Island Rail Road (LIRR) and Metro-North Railroad in New York City, NJT in New Jersey, the Massachusetts Bay Transportation Authority in Boston and Metrolink in Southern California. For freight railroads, around 600 freight railroads operating in the USA are privately owned and operated with a nearly 140,000-mile rail network (AAR, 2017). Seven freight railroads (e.g. BNSF, Canadian National, Canadian Pacific, CSX Transportation, Kansas City Southern, Norfolk Southern and Union Pacific) with at least US$447.6m in 2016 revenue are classified as Class I railroads and each operates in a variety of states over thousands of track miles. Based on the latest statistics from AAR (2018) , seven Class I railroads contribute to around 69% of freight rail mileage, 90% of employees and 94% of revenue. In addition, hundreds of short line and regional railroads transport the goods across the country in various operation sizes.

Traincrew members include engineers, conductors or assistant conductors in some cases. FRA (2016a) established minimum requirements for the size of train crew staffs depending on the type of operation and the safety risks. Generally, train engineers and conductors make up the train staff in either freight trains or passenger trains and have the responsibility for safe train operation, as well as providing the operation reports and problem reports to the railroads. To guarantee it, train engineers and conductors are subject to a federally regulated training, qualification, and certification process mandated by 49 Code of Federal Regulations (CFR) Part 240 and Part 242, respectively.

Furthermore, effective communication channels between the hierarchical levels are essential ( Figure 2 ). For example, in the communication channels between US Congress and FRA, Congress establishes and enacts legislation as well as grants budgets to FRA. In return, the FRA needs to submit government reports so that Congress can attain information on proposed legislation, oversee the activities of the government agency and evaluate the implementation of federal laws ( GPO, 2018 ). In terms of the connections between FRA and railroads, the FRA has the responsibility for making regulations and certifications for the railroad industry, as well as the supervision of railroads’ execution, in the USA. The rules and regulations are published in the form of Federal Register and the CFR. Some safety recommendations and standards are also published by FRA, such as a safety advisory to remind railroads of the significance of compliance with restricted speed operating rules ( FRA, 2012 ), an updated passenger equipment safety standard for high-speed trains that can travel up to 220 miles per hour ( FRA, 2016b ). Conversely, railroads must work out necessary accident/incident reports, implementation plans and operations reports. Moreover, the PTC system in the operating process ( Figure 1 ) is excluded in the control structure at passenger stations ( Figure 2 ) because current regulations ( FRA, 2011 ) designate train operations at passenger stations as a regulatory exemption from the PTC requirement, which would be further discussed in following sections.

5. Case study in New Jersey Transit accident at Hoboken Terminal

5.1 accident narratives.

Most accident information and probable causes mentioned in this paper refer to NTSB accident investigation reports ( NTSB, 2018a , 2018b , 2018c ) and the FRA database ( FRA, 2018 ). More accident details and investigation results are also available in these references. This section only briefly summarizes crucial accident information to support the analysis of end-of-track collisions at passenger stations using STAMP model.

An NJT collision accident at Hoboken Terminal occurred on September 29, 2016, at about 8:38  a.m. (Eastern Standard Time). An NJT train failed to stop short of the stub end of track 5 and overrode a bumping post, which is a rigid structure that is level with the train’s coupler at the end of the track, then the train struck a wall of the Hoboken Terminal in New Jersey, USA ( Figure 3 ). NJT is a state-owned public transportation system that has served the state of New Jersey since 1979. It connects the major commercial center and employment hubs within New Jersey, as well as some neighboring major cities of New York and Philadelphia. NJT is the nation’s largest statewide public transportation system that provides around 270 million passenger trips totally in the fiscal year 2017 ( NJ Transit, 2018 ).

According to the locomotive event recorder data released by NTSB (2018b) , the train was traveling about 8 mph at about 38 s before the collision and the throttle position went from idle to the number 4. As a result, the train speed started to increase and reached about 21 mph. Just less than 1 s before the collision occurred, emergency braking was applied by the engineer and train speed at the time of the collision was still documented as 21 mph in the locomotive event recorder. The accident train includes one cab car, three passenger cars and one locomotive at the rear with about 250 passengers and three crewmembers (engineer, conductor and assistant conductor). A total of 110 people got injured and one person on the passenger platform was killed by the falling debris ( NTSB, 2018b ). The total damage to the equipment, track, signal and structural damage was over US$6m ( FRA, 2018 ).

5.2 Stamp-based analysis in New Jersey Transit accident at Hoboken Terminal

As an end-of-track collision at terminal, the Hoboken accident roughly has the identical operation safety control structure as developed in Figure 2 . The general system hazard related to NJT accident is identified as a failure to stop at the end of the terminating track where it struck the bumping post. This hazard is restrained through the constraints that are applied by the entire socio-technical control structure to enforce safe train operations at passenger stations. Instead of distributing blame or responsibility to any controller, following further discussions aim to understand the occurrence of the NJT accident and to analyze its inadequate control actions in this complex train operation system at the passenger station. Detailed analysis of inadequate enforcement is extended with selected system components’ safety constraints, failures or inadequate control actions and supportive backgrounds, as shown in Figure 4 .

5.2.1 Federal Railroad Administration.

As is introduced in Section 4.2, the FRA holds the primary responsibility for developing and promulgating legislations, regulations and policies. Three Codes of Federal Regulations, namely, 49 CFR Part 238, 49 CFR Part 229 and 49 CFR Part 236, involve direct applications of rail operation at passenger stations (e.g. Hoboken Terminal). In 49 CFR Part 238, both locomotive and passenger car equipment is required to be inspected and maintained periodically. Based on requirements in 49 CFR Part 229, an alert, as a safety device in a locomotive cab, is used to monitor engineer-induced control activities and promote engineer attentiveness. If no control activity is detected in a system by the engineer within a predetermined time, both audible and visual alarms will be activated to prompt a response. In addition, 49 CFR Part 236 defines a terminal track as a mainline track exclusion addendum, in which train operation is limited to a restricted speed. According to the railroad accident brief investigated by NTSB (2018b) , these three FRA regulations and policies published were strictly followed by NJT. More specifically, in the mechanical part, the inspection and maintenance program of NJT met the requirements in 49 CFR Part 238. Before the trip, a comprehensive inspection was made on the accident controlling cab car and the air brake based on FRA requirements. In addition, an alerter was installed in the locomotive cab, which was operated properly as was required by 49 CFR Part 229. In terms of signal part, the signals indicating restricted-speed operation, together with other wayside signals, were inspected and verified for a proper performance, and there was no deficiency in the rate of cab signal code, either. NTSB (2018b) concluded that both the signal system and the train control system were functioning as was designed, which were in accordance with the FRA requirements. Meanwhile, NTSB (2018b) pointed out that there was a lack of legislative rules or non-legislative recommendations providing medical standards or regulations to address OSA screening and treatment.

According to NTSB (2018a) , OSA is a contributing factor in the NJT accident and several previous train accidents, because it is able to result in frequent interruptions in sleep during train operation, which leads to an expanded fatigue and daytime microsleeps. Since 2010, NTSB (2018a) has investigated 6 OSA-related railroad accidents causing nine fatalities and 283 injuries in total, and identified that the sleep disorders were a key medical fitness issue for train employees. As a result, a variety of safety recommendations have been subsequently made to the FRA, such as R-12–16 ( NTSB, 2012 ), R-13–21 ( NTSB, 2013 ) and R-16–044 ( NTSB, 2016 ), all of which suggested that the FRA should develop and enforce its standards to medically screen railroad employees for sleep apnea and other sleep disorders. However, according to NTSB (2018a) , it was still in a process of responding to the reiterated safety recommendations, and there was no medical standard or regulation directly addressing OSA screening or treatment mandated by FRA in the NJT accident.

A nationwide implementation of the PTC system is mandated by the Rail Safety Improvement Act of 2008. ACSES II, one of PTC system types approved and certified by FRA, was implemented by NJT to prevent human-error-related train accidents through automatically slowing down or stopping trains. However, train operation at passenger stations is designated as a regulatory exemption from the PTC requirements based on current FRA regulations ( FRA, 2011 ). Thus, stopping a train in a terminating track would depend on the attentiveness and compliant behavior of the engineer.

5.2.2 New Jersey Transit.

As a statewide public transportation system providing around 270 million passenger trips each year ( NJ Transit, 2018 ), NJT is responsible for strictly following safety requirements and constraints to mitigate operational risks. Firstly, NJT should make sure that mechanical components work without defects according to FRA requirements. It should also keep periodic inspections and maintenance programs for the locomotive and passenger cars to meet the FRA requirements. Moreover, trainings and physical examinations for crew staff are also essential. NJT has the responsibility for continuing educational requirements on train crew staff to maintain their competence and knowledge about rail safety. In the personnel physical condition, NJT is accountable to providing a periodic physical examination to ensure that the crew members, particularly those at safety-sensitive positions, are fit for their duties. Furthermore, according to NTSB reports ( NTSB, 2018a , 2018b ), the system safety program plan (SSPP) and OSA screening were two safety constraints involving inadequate control actions, which were identified by NTSB as probable contributing factors to the NJT accident at Hoboken Terminal.

SSPP is a system safety program designated to assist in operation monitoring and appropriate data collection, so as to identify emerging safety issues before the occurrence of accidents, in which the significance of hazard management was recognized both by FRA and APTA ( NTSB, 2018a ). APTA (2006) identified SSPP as the first element of a formal process in the application of the principles of system safety, which is described as a structured program with a proactive process and procedure to identify and eliminate hazards as well as the risks resulted to the railroad system. A total of 23 elements are identified in the Manual for the Development of System Safety Program Plans for Commuter Railroads ( APTA, 2006 ) for commuter railroads to consider in the development of an SSPP. Based upon NTSB investigation ( NTSB, 2018a ), although NJT had its SSPP in effect at the time of the accident, it lacked an identification and evaluation of the potential of a collision between a train entering the stub-end track and the bumping post. In addition, NJT designated terminal tracks at Hoboken Terminal as other-than-main line tracks and exempted them from PTC requirements in accordance with FRA requirements ( FRA, 2011 ). In this context, train operation at Hoboken Terminal largely relied on train engineers, who had a severe OSA that was not diagnosed or treated.

5.2.3 Train engineer.

Train engineers play a pivotal role in the safety of both passengers and bystanders while operating locomotive equipment. The train engineer has the responsibility to make sure the train is in compliance with all signals, rules and regulations at the Hoboken Terminal. Some additional safety requirements were also followed by the train engineer. For example, the inspection of train equipment and cabs on locomotives before departure was conducted by the train engineer to make sure these were in appropriate working order. Train engineers should receive and transmit information via the radio or telephone to the conductors and dispatchers and should also be aware of the surrounding areas and necessary decision making accordingly. Nonetheless, the train engineer in this accident train failed to follow the speed limits and restricting signal. As a result, the train speed was reduced with insufficient braking distances to the end of the track by the engineer and then led to the occurrence of the NJT accident. In respect of train crews, the train engineer’s increased fatigue due to frequent interruptions in sleep contributes to failing to stop the train after entering Hoboken Terminal.

As another recent high-consequence end-of-track accident at passenger station, the LIRR accident at Atlantic Terminal, New York on January 4, 2017, had similar inadequate enforcement of control actions and contexts comparing against NJT accident at Hoboken Terminal based upon investigation results from NTSB (2018a) . It would not be further discussed in this paper and sufficient precise accident details and investigation results are also available in NTSB reports ( NTSB , 2018a, 2018c ).

6. Discussions in policy implications and practices

The end-of-track collision at Hoboken Terminal discloses a potentially stern consequence of accidents at passenger stations, in which a train fails to stop before reaching the end of its terminating track. The STAMP-based analysis with selected accidents contributes to a distinct understanding of system hazards, constraints and the hierarchical control structure of train operation at passenger stations. Based on the analytical results, in particular with inadequate control constraints, this section aims to inform several effective safety strategies to reduce accident risks at passenger stations and promote their safety level in the future. After the occurrence of the NJT train accident in 2016, several safety issues were raised in the railroad industry, such as the measures ensuring that engineers are fit for their duties, investigations on PTC system at terminal tracks and the implementation of safety management systems. The findings are discussed in the following subsections based on both the STAMP-based analysis of end-of-track collisions and reference information in NTSB report ( NTSB, 2018a ), including OSA screening and treatment; mechanisms to automatically prevent end-of-track collisions; comprehensive system safety program plans; and bumping posts with a higher impact tolerance.

6.1 Obstructive sleep apnea

OSA is one major contributing factor to fragmented sleep and subsequent daytime fatigue sleepiness, which could be a crucial increasing risk for train crewmembers. In a study of train engineers in Greece, Nena et al. (2008) concluded that OSA was common among Greek railway drivers and 62% of train engineers encountered this sleep-disorder issue, while the percentage of adults having OSA in the general population from Western countries was only around 5% ( Young et al. , 2002 ). Similarly, Koyama et al. (2012) studied the prevalence of OSA among Brazilian railroad workers. Based on an evaluation of a survey from 745 railroad workers, the prevalence of OSA was approximately 35%, which is higher than that in the general population too. Without OSA screening and diagnosis programs required by federal agencies or railroads, a relatively high prevalence in the train engineers of OSA would possibly increase the risk of end-of-track collisions at passenger stations. According to an NTSB report ( NTSB, 2018a ), the engineer in the NJT accident underwent a post-accident study and was diagnosed with severe OSA, which was a probable cause of this accident. Nevertheless, at the time of the collision, there were no regulatory guidelines or recommendations referring to effective diagnosis and follow-up medical treatment in respect to OSA. This STAMP-based accident analysis demonstrates the necessity for government agencies, railroad associations and railroad companies to work closely to promote the development and enforcement of a complete, effective program involving OSA screening and follow-up medical treatment. To achieve this, extensive research studies could contribute to the development of an effective OSA program. Romero-Corral et al. (2010) disclosed the interactions between body weight (measured by BMI) and OSA, which were also used in the investigation of NJT train engineer after accidents by NTSB (2018b , 2018c ). Epstein et al. (2009) provided a comprehensive clinical guideline for the evaluation and treatment of OSA. The diagnostic of OSA was suggested to involve a sleep-oriented history, physical examination and objective testing. Once the diagnosis is set up, the patient should consider an appropriate treatment strategy that covers positive airway pressure devices, oral appliances, behavioral treatments, surgery and/or adjunctive treatments ( Epstein et al. , 2009 ). With the support from existing but limited OSA screening practices and literature, a comprehensive, valid OSA program can be developed to mitigate the risk from OSA posing to intercity passenger trains and commuter trains. An intervention policy with regulatory guidelines and recommendations referring to diagnosis and follow-up medical treatment are paramount to detect OSA and other sleep disorders among train crewmembers. In this case, the railroad employees in safety-sensitive positions should meet medical standards to be fit for duty, which is able to reduce such human-factor-related train accidents.

Figure 5 provides a visual interpretation of the proposed recommendation in the STAMP model. More specifically, a practical OSA program involving both screening and treatment can be developed based on the guidance from NIH and would be valid for train crew members who should be fit for duty under the OSA program to release more positive commands in the train operations at passenger stations.

6.2 System safety program plans

As mentioned in Section 5, NJT had SSPP with rich hazard management to monitor train operations and collect considerable data to identify emerging safety issues. Although six collisions have also occurred between NJT trains and bumping posts between 2007 and 2016 (two of them at Hoboken Terminal), NTSB (2018a) pointed out that NJT did not recognize the risk of an end-of-track collision at passenger stations as a key risk factor in SSPP. Similarly, the SSPP overlooked the need for OSA screening and treatment to prevent potential hazards and did not account for undiagnosed or untreated OSA. Therefore, SSPP should be promoted and updated with the account for the increased risk of OSA and operational hazards associated with end-of-track collisions. Eventually, the robust SSPP documenting comprehensive hazards can contribute to the mitigation of emerging, critical risk elements through an effective management system.

In Figure 5 , the proposed robust SSPP is interpreted visually using simplified STAMP. In addition to adding end-of-track collisions and OSA into the SSPP, federal agencies (e.g. FRA or FTA), industry associations (e.g. APTA) and railroads can construct a reliable SSPP with comprehensive hazards documented. This action would promote the level of safe train operations (commands in Figure 5 ) by train crewmembers. Moreover, the reports and feedbacks from the train operation process can also advance an exhaustive, continuous safety management system and eventually mitigate the risk at passenger stations before the accident occurred.

6.3 Collision avoidance and mitigation techniques

6.3.1 positive train control..

NJT designated the terminating tracks as “other-than-main-line track” and exempted them from PTC implementation requirements, which are in accordance with federal regulation ( FRA, 2011 ). Without the implementation of PTC system that can automatically prevent these passenger trains from human-error-related accidents, the safe train operations would generally depend on crewmembers’ compliant behavior when they are entering passenger stations with stub-end tracks. However, NTSB (2018a) augured that it cannot provide the level of safety necessary to protect the public. In the study of the safe approach of train terminals, Moturu and Utterback (2018) stated that implementation of a design mitigation (e.g. PTC) has distinct benefits for controlling speed entering terminal point locations. Therefore, it is critical to implement a mechanism that can automatically stop a train before the end of the tracks even if the engineer is negligent or disengaged to mitigate potential hazards to passengers and bystanders at passenger stations.

The train operation procedures that NJT was using during the accidents are shown in Figure 6(a) , and the train movements were only managed by train crewmembers. If appropriate wayside signal and cab signal are displayed, the safe train operations at passenger stations would be guaranteed by the compliant behavior of train crewmembers and any performance that does not follow received information is likely to result in hazards or even accidents. A train control system, such as PTC, can be implemented to prevent such train accidents attributable to human error. The train movements are still under the control of train engineers but are also monitored by PTC. Taking ACSES, one of PTC technologies that are primarily used on the Northeast Corridor and mostly implemented by Amtrak and commuter railroads (e.g. NJT and LIRR), as an example, it integrates the locomotive computer, wayside device, communication network, transponders and back office to collect and analyze train real-time status, movement authority and speed restriction information (measured variable from sensors to PTC in Figure 6(b) ). If the train crewmembers fail to appropriately operate train movements, ACSES would automatically apply the brakes and bring the train to a positive stop ( Zhang et al. , 2018 ).

6.3.2 Concept of operations for positive train control enforcement at passenger stations.

To explore how ACSES may function (what is needed, how to implement it) as if the ACSES was enforced under restricted-speed operations at passenger stations, specific modifications are proposed in Figure 7 based upon Zhang et al. (2019) . This concept of operations focuses on the PTC enforcement to prevent end-of-track collisions at passenger stations and does not intend to propose this system to be installed everywhere in the US rail system.

Figure 7(a) shows a stub-end passenger station with a bumping post locating at the end of tracks. The proposed solution is to divide the station into two zones as shown in Figure 7(b) . As the train reaches the end of full ACSES territory, the last transponder set tells the onboard ACSES system that it has entered “Out of ACSES Territory.” No linking distance will be provided to the next transponder set, but the transponder set will provide a line speed package designed for the maximum speed that trains are supposed to be operated in the terminal area (e.g. 15 mph). The preceding transponder set will be designed with a permanent speed restriction package telling the system that the speed will be capped at the lower speed at the location of the end of ACSES territory. The second zone begins at the entering end of each platform. The first transponder set (T1) makes the system re-enter ACSES territory and provides positive train stop (PTS) information targeting the end of the platform track or bumping post as the stop target. This transponder set also provides linking distance information to the next transponder set (T2). The first transponder set needs to be located at a distance greater or equal to the braking distance needed to stop the train. The second transponder set (T2) provides redundancy to the first set and also better stopping accuracy. This transponder set provides a PTS with the distance to the bumping post.

As the train reads the first transponder set T1, the engineer will receive an alarm. The system calculates a braking curve based on the present speed of the train and the distance to the target (bumping post). Provided there is sufficient braking distance, the train will receive a stop enforcement unless the engineer stops the train before it reaches the target. If there is insufficient stopping distance, the system will slow the train to a much lower speed than the train would have been traveling without this solution. When the train changes direction, it will read the transponders T2 and/or T1 in the reverse direction. The message in these transponder sets for this direction will tell the train that it is leaving ACSES territory until it reaches the location where ACSES territory with full supervision starts ( Figure 7(b) ).

Moreover, the mechanisms to prevent trains from colliding with the end of tracks are not limited to PTC systems. Any PTC-alike collision risk mitigation technology can be developed and implemented to achieve the same prevention function and safeguard. Furthermore, some challenges and the impracticability of additional technology installation, such as the complexity of terminating tracks, the size of the turnouts, the large number of train movements and the close proximity of signals and switches at passenger stations should be taken into account while developing and implementing practical end-of-track collision mitigation techniques.

6.4 Bumping post with more impact tolerance

Bumping post, also known as bumper block, buffer stop, is an attenuating safety device placed at the end of terminating track to stop unauthorized movement. In the NJT accident at Hoboken Terminal, the bumping post (an exemplar shown in Figure 8(a) ) located at the end of the tracks was overrode and destroyed by the accident trains. NTSB (2018a) concluded that the bumping post at the accident location did not by itself provide protection at passenger stations adequately. The fixed bumping posts, of the type employed at Hoboken Terminal can only offer tolerance and protection for low-speed impact. In theory, a train transfers enormous kinetic energy to the bumping post in an impact (e.g. end-of-track collision) and can easily exceed the bumper’s tolerance. After hitting the bumping post, the accident train stuck a wall of the terminal and also led to one person on the passenger platform died due to the falling debris from the Hoboken Terminal.

In addition to the fixed bumping post that was implemented in the NJT accident, energy absorbing bumping posts ( Figure 8(b) ) are dynamic barriers that utilize friction mechanisms and hydraulic systems and can absorb relatively higher-speed impact. However, NTSB (2018a) pointed out that most terminals do not have the physical space for this type of bumping post, in particular for the friction mechanisms with extensive distance demand. Moreover, Moturu and Utterback (2018) identified that energy absorbing bumping posts are still limited in the amount of kinetic energy that they can tolerate and would have a large likelihood to fail at speeds over 10 mph. It means even if this type of bumping post was equipped, it still cannot bear the impact of the NJT train at Hoboken Terminal, which was traveling around 21 mph at the time of the accident. Therefore, it is essential to design and implement bumping posts with both higher impact tolerance and more practical function in the complex station areas. Figure 8 visually indicates how advanced bumping posts increase the level of safe train operations at passenger stations. Advanced bumping posts located at the end of the terminating track can strengthen the impact tolerance to the uncompliant train movements. As a result, the potential collision consequence (process output in Figure 8 ) under reinforcing collision protection device would be reduced.

It is acknowledged that there is always an upper limit of allowed impact speed for this impact absorbing device. In practice, bumping post should be coupled with the aforementioned end-of-track collision risk mitigation strategies (e.g. OSA screening program, train protection systems, comprehensive SSPP) to effectively prevent end-of-track collisions in the nationwide rail system.

7. Conclusion

End-of-track collisions at passenger stations have caused substantial damage costs and casualties over the past decade in the USA. At present, the safety of train operation on terminating tracks would generally depend on the attentiveness and compliance of crewmembers. One recent end-of-track collision at Hoboken Terminal discloses the potentially high consequence of train operation at passenger stations and is analyzed through STAMP, a widely used system-based accident model in complex systems. The analytical results demonstrate an explicit understanding of system hazards, constraints and the hierarchical control structure of train operation on terminating tracks in American railroads. In particular, the failures or inadequate control constraints in operating process loops primarily play a probably contributing role in the high-profile end-of-track collision.

Four policy recommendations and practical options are discussed to improve the safety status and mitigate the risk of end-of-track collisions at passenger stations based on recommendations of the NTSB and our engineering assessment. Firstly, it is essential to ensure an effective screening and treatment program of sleep disorders to mitigate the noncompliant behaviors of railroad employees at safety-sensitive positions. Secondly, mechanisms (e.g. PTC) are needed to automatically prevent collisions between trains and the end of tracks in case the engineers are inattentive or disengaged. Thirdly, an effective SSPP should be comprehensively promoted and updated with identified hazards (e.g. end-of-train collisions, OSA) to protect the train operation against the increasingly unsafe conditions. Fourthly, a bumping post with a higher impact tolerance should be designed and implemented at the end of the tracks to absorb the trains’ kinetic energy and reduce bad consequences. The findings of STAMP-based analysis can serve as valid references for policymakers, governmental accident investigators, railway practitioners and academic researchers. Ultimately, they can contribute to establishing effective emergent measures for train operation at passenger stations and promoting the level of safety necessary for protecting the public. The STAMP accident models developed in this paper can also be adapted to the studies and investigations of other train accidents as well as railway systems in the USA.

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Basic process model in train operation at US passenger stations

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Basic train operation control structure at passenger stations

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Map of Hoboken Terminal tracks

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STAMP analysis of control structure and system components with inadequate constraints (summarized based on NTSB reports)

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Illustrations of proposed programs in the prevention of end-of-track collision in STAMP operating processes

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Illustrations of proposed end-of-track collision prevention solutions in STAMP operating processes

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Architectures of passenger terminal station (a) without ACSES enforcement and (b) with ACSES enforcement ( Zhang et al. , 2019 )

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Exemplar bumping posts: (a) fixed bumping post and (b) energy absorbing bumping post ( Cortez, 2016 )

Selected accident models used in diverse literature

Explanatory descriptions of terms in STAMP process model

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The research was completed when the lead author was a graduate research assistant at Rutgers University. The work has been partially funded by the Federal Railroad Administration (FRA) of the US Department of Transportation (USDOT) (Contract No. DTFR53-17-C-00008). However, the views and opinions expressed herein are those of the authors and do not necessarily state or reflect the views of USDOT or FRA, and shall not be used for advertising or product endorsement purposes.

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How to Use Case Studies in Your Employee Training Sessions

Case studies can be powerful tools for learning and training. They're evidence-based stories that showcase the outcomes you want, so using them as the basis for your training can make the training itself more engaging and more effective. The question is, how can you use a case study to enhance your training for learners? There are several options.

case study on train

  • Identify personal leadership styles
  • Capitalize on style strengths
  • Minimize style trouble spots

Table of Contents

Design a case study to fit the training, develop training to fit a case study, use a longitudinal case study to demonstrate outcomes of training, use miniature case studies to prove individual points, thread a case study throughout training, ask trainees to predict case study outcomes, discuss potential alternative outcomes in case studies, turn a case study into an immersive simulation, create a framework case study and encourage trainees to fill it out.

First up, you have one major decision to make. Do you design training around a case study, or do you design a case study to fit your training? Both perspectives are equally valid as long as the study results and the training program goals are aligned.

Let’s say you choose to design a case study to fit your desired training. For example, you're trying to implement the  Delivering Exceptional Phone Service  reproducible training course for your customer service team. To back up the training, you want a case study that showcases how putting the techniques taught in the course into practice will bolster positive outcomes with customer service.

Designing a Case Study

You have two options here.

  • The first is simply writing a case study based on your own experiences, accentuating the necessary details relevant to the training, and pruning it down to the bare essentials to prove your point.
  • The other option is to seek out existing case studies performed by renowned research firms that support your points.

In either case, you can then use the case study as a "real world" example of how the techniques in the training can be put to actual use and how they tangibly impact positive outcomes. Make sure to highlight specific aspects of the case study and how they relate to the practices put forth in the training module for better retention.

Your second option, as an alternative, is to develop your training to fit an already existing case study.

Developing Employee Training

The process looks a little something like this:

  • Begin by finding a case study that results in the outcomes you're seeking. For example,  this case study from Train Like a Champion  focuses on getting training to produce long-term results, something that every company can benefit from implementing.
  • Next, review the case study. Look for salient details and mechanisms used to achieve the outcomes you desire. Ideally, the case study itself will support those mechanisms and expound upon how to use them.
  • Finally, develop a training module that integrates the case study and its data, as well as the mechanisms you uncovered, to train your employees to achieve those same outcomes.

You can accompany the training module with the case study, with details and data uncovered along the way, or you can use it as a companion piece or use it as cited sources or proof for the claims you're making. None of these choices are inherently wrong, so pick the ones that work best with your staff and your means of training to create a better learning experience.

Longitudinal case studies are case studies that look at and measure specific data about their subjects over a long period. Such case studies can follow individuals throughout a particular period of years, their careers, or their entire lives. For example, longitudinal studies are often used in medicine to help study the long-term effects of various substances and illnesses.

A longitudinal case study can be a powerful tool for building training. You can point to specific, hard evidence that certain kinds of training not only improve short-term results and benefits for employees, clients, and companies but can increase the value of employees throughout their careers.

Demonstrating Outcomes of Training

Using this kind of case study can be an essential part of encouraging your employees to take the training seriously. After all, it's one thing to encourage employees to participate in training because it benefits customers or the company, but it's quite a different incentive if you can showcase how that training will improve their career prospects.

The tricky part about this is that case studies can prove many different points because different people have different career trajectories and leverage different skills in different ways. That is why it can be essential to begin with training modules such as  What's My Leadership Style  to help employees identify which individuals to follow in the case study and which outcomes are most relevant to their specific situations.

If finding specific, relevant longitudinal studies isn't possible, an alternative approach involves leveraging small-scale case studies to reinforce key points throughout your training process. For example, throughout a comprehensive  customer service training  course, you can use specific case studies that highlight varied responses to an irate customer, showcasing how different approaches lead to distinct outcomes. These case studies provide tangible examples to support decisions about adopting a placating, resistant, or combative tone in customer interactions.

Using Miniature Case Studies

The benefit to this option is that there are, in general, many more small-scale case studies than there are more extensive, longitudinal case studies. Moreover, it's much easier to find them and use them to prove your points. Long-term case studies can have surprising outcomes, and they can have findings that contradict your studies and policies. That can be difficult to reconcile unless you're willing to wholly adjust your training and direction.

The biggest potential drawback to this option is that there are many small-scale case studies, many of which can have contradictory outcomes. With the vast pool of small-scale case studies available, there is a risk of cherry-picking examples that selectively support a specific viewpoint, regardless of their overall value. This practice could compromise the integrity of the training content and may not provide a holistic representation of the topic at hand. Trainers should exercise caution and ensure that the chosen case studies are relevant, unbiased, and contribute substantively to the overall learning objectives.

If you think back to some of the more effective textbook designs for schools in higher education, you may find a through-line. Many effective textbooks include an ongoing, long-term set of examples, or "characters," they follow along the way. For example, in courses where you learn a language, a textbook will often have a set of characters who interact in varying situations to showcase quirks of language, particularly conversational use of the language.

A case study can be used in this manner for your training. Fortunately, many comprehensive and overarching training courses have these kinds of examples and case studies built into them.

Threading Case Study

The goal is to allow your trainees to explore training in a multifaceted way. That might include links to studies, links to infoboxes, video interviews, and much more.

An added benefit of this training method is that you can make a single training module much more comprehensive in terms of answers to common and uncommon questions. Training employees from a point of knowledge can be surprisingly challenging because it can be tricky to judge even what the trainees don't know. Providing in-depth, interlinked, embedded answers to questions for trainees to explore helps bring everyone to the same page.

One thing that sets effective training apart from ineffective training is the level of interactivity. When training is interactive and engaging, trainees learn much more from it by participating in "real-life" examples and demos of the training in action. This approach enables participants to apply their knowledge in real-life situations, promoting a deeper understanding and emphasizing their problem-solving ability to choose appropriate resolutions.

Predicting Case Study Outcomes

One way to help encourage engagement in training is with a case study that puts that training into action. Divide the case study between setup and resolution, and have the trainees read the setup portion of the training. Cut it off as the individuals in the case study are making their decisions based on the training (or ignoring the training).

Then, ask the trainees to predict what the outcomes will be. Encourage them to write down their predictions. Then, you can progress with the case study and reveal the actual results of the training. While some case studies may follow predictable paths, introducing occasional curveballs keeps participants on their toes. These unexpected twists challenge trainees’ critical thinking skills and their ability to adapt their problem-solving strategies. You can then discuss why they made the predictions that they did and what led them to their decisions, whether right or wrong.

This interactive approach not only transforms training into a participatory experience but also creates a platform for meaningful discussions.

Like the above, you can leverage case studies and predictions to speculate. How would the outcome have changed if the individual in the case study made a different choice or acted differently?

Potential Alternative Outcomes

What changes would your employees make?

"After reading a case study together or independently, you can have your participants write a different ending to the case study. For example, if you read a story about a woman who improved her communication skills after attending a workshop (just like the one your students might be in), have them write what would happen if she didn't attend the workshop. Have them write what would happen if she was engaged/not engaged. Ask them to consider what is going on in the woman's life that might impact her ability to communicate appropriately or efficiently during the time of training. Writing a different outcome prompts participants to consider the whole story and not just the parts that are presented to them." –  TrainingCourseMaterial

For an interesting case study of your own, you can ask your trainees to read a situation and convey how they would act in that situation before implementing the training in the first place. Then, progress through the training modules. When finished, ask the trainee to revisit, see how accurate their behavior is to the goal, and ask them what changes, if any, they would make.

Once again, studies show that the best training is training produced in the form of an immersive simulation.  

Look for industry case studies about particular incidents.  Several agencies  produce comprehensive investigations into the circumstances behind industrial accidents, often in factory, warehouse, or shipping processes. These case studies can form the basis of a scenario wherein you ask your employees to role-play how they would respond if the incident occurred in your facility.

You can then use the realities of the investigation to enforce consequences in the simulated disaster. For example, say you're training employees to handle a chemical spill in a warehouse. The established procedures outline specific actions to be taken. Within the simulation, introduce a scenario where one employee is found unconscious within the chemical spill. This introduces a critical decision point: will someone attempt a rescue, and if so, will they do so without proper preparation? You can then remove this individual from the training scenario because their actions led to them being incapacitated.

Immersive Simulation Case Study

There are many such examples. Always remember that most, if not all, industrial and commercial regulations are built on the back of people dying because of loopholes or unforeseen circumstances.

This approach allows employees to engage with the training material in a hands-on, realistic manner. It not only reinforces the importance of adhering to established protocols but also highlights the potential repercussions of deviating from proper procedures. The immersive nature of these simulations helps employees internalize the lessons, making the training more impactful and applicable to their day-to-day responsibilities.

Finally, another way to use case studies for training is to turn your trainees into case studies themselves. Build a framework or a template of a case study, with questions about the scenario, their responses, the training, and their behavior after the training. Encourage trainees to fill out these case study templates, then participate in training, and fill them out again. For added value, track these employees for months afterward to see where they've gone, how they've implemented their training, and how it has improved their careers.

Framework Case Study

The use of case studies can be a powerful training tool, but they can only be effective if coupled with practical training modules. After all, you can't know how to reach your goals without knowing where you are. That's why we offer dozens of training options in our reproducible training library, as well as dozens more assessments (both instructor-led and self-guided) to help establish baselines and build awareness.

Check out our training library, and find case studies that align with your company values and learning objectives.

To learn more about how to help your employees, check out our  What’s My Leadership Style  course. This course is a management development tool, leadership style assessment, and online training workshop. This comprehensive tool is designed to pinpoint an individual's leadership style, offering valuable insights for organizational leaders, managers, and supervisors. By utilizing this tool, professionals can enhance their performance and cultivate the skills necessary to evolve into effective and impactful leaders within their respective roles.

Do you have any questions or concerns about using case studies in your employee training sessions to provide the best outcomes for your learners? If so, please feel free to leave a comment down below, and we'll get back to you! We make it a point to reply to every message we receive, and we would be more than happy to assist you or your company however we possibly can.

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About our author

Bradford r. glaser.

Brad is President and CEO of HRDQ, a publisher of soft-skills learning solutions, and HRDQ-U, an online community for learning professionals hosting webinars, workshops, and podcasts. His 35+ years of experience in adult learning and development have fostered his passion for improving the performance of organizations, teams, and individuals.

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Case Study: Chatsworth Train Collision

Everyone seems to agree that root cause analysis is about solving problems, but there’s no agreement as to how a root cause analysis is done.

Were Lessons Learned?

On September 12, 2008, a commuter train and a freight train collided head-on in the Chatsworth district of Los Angeles, killing 28 and injuring more than 100. The crash was the worst train accident in 15 years and taxed emergency resources. The accident made national headlines and raised many questions about the safety and reliability of the nation’s rail system. A root cause analysis case study of this accident can help us understand what caused this accident and its aftermath.

A Cause Map™ Diagram, a Visual Root Cause Analysis

A Cause Map diagram is a visual root cause analysis that intuitively lays out the causes that contribute to an issue and shows the cause-and-effect relationships. The first step in the Cause Mapping ® process is to define the problem and we do this by filling out an outline with the basic background information as well as how the issue impacted the goals. A simplified example of a completed Outline for this root cause analysis case study is below.


Once the Outline is filled in, the Cause Map diagram is built by starting at one of the impacted goals and asking “why” questions. Any of the goals can be used as a starting point, but all the goals should eventually be added to the Cause Map diagram. The Cause Map diagram is expanded until all relevant information is captured. Click on the thumbnail below to see an intermediate-level Cause Map diagram for this root cause analysis case study.


The Investigation Findings

The National Transportation and Safety Board (NTSB) investigated this train collision. You can read the full report here . The investigation team determined that the freight train had the right of way and that the commuter train had traveled through a red stop signal. A red signal is common in this location because it is single track. There are three tunnels that are only wide enough to support one train so the trains moving in opposite directions take turns. The track was originally built in the early 1900s and it would be very expensive to widen the tunnels.

The NSTB determined that it is likely that the train engineer was texting and was distracted when the commuter train passed the red signal. Cell phone records showed that the commuter train engineer texted a message 4:22:01 pm and received one at 4:21:03 pm and the accident occurred at 4:22:23 pm. Additionally, the engineer received 7 and sent 5 texts between 3:00 pm and the time of the accident.

Additionally, both engineers were unaware of the other train until it was too late to avoid a collision. There is limited visibility on this section of the track because it is curved. Sight distance testing by the NTSB determined that the train's engineers could not see the other train until less than 5 seconds before the collision, which is not enough time to brake and prevent the accident.

One of the most important findings by the NSTB was that the installation of a positive train control (PTC) system could have likely prevented the accident. A PTC system monitors and controls train movements and would have automatically braked the commuter train when it passed the red stop signal.

Could it Happen Again?

There were many lessons learned in this incident. The nation is working on improving the safety of the rail system to prevent similar accidents, but it is still very much a work in progress.  

As a direct response to this accident, Congress passed the Rail Safety Improvement Act of 2008, requiring Class 1 Railroad mainlines with regularly scheduled intercity and commuter rail passenger service to fully implement PTC by December 31, 2015. Unfortunately, few railroads met that deadline to implement PTC stating that the technology was too complicated and not fully developed. The timeline was extended to December 31, 2018, with a provision to extend the deadline to December 31, 2020 if PTC implementation plans were submitted by end of 2018.

The commuter railroad that was involved in 2008 Chatsworth train collision, Metrolink, was the first commuter system to deploy PTC technology and it is fully active on the 341 miles of track owned by the Metrolink. There are 171 miles of Metrolink track that are owned by freight lines and they are reported to be working towards installing PTC.

While the work to implement PTC is underway, rail accidents are still occurring, although none have been on the scale of the Chatsworth collision. According to the NSTB, there have been at least 21 deaths and 364 injuries in the 10 years since the Chatsworth collision occurred.

How Much is Safer Worth?

Few of us probably work in the rail industry, but there are many lessons that can be learned from studying the Chatsworth collision and PTC. In this root cause analysis example , a single error by one individual resulted in fatalities. Ideally, all work processes would be built with enough redundancies that a single-point failure, whether it be an operator missing a signal, a component failure or a software bug, would be caught and corrected before anybody gets hurt.

Adding redundancy and safety improvements costs both money and time and attempting to add layers of safety can add complications. Every organization has to weigh the options and decide what level of risk is acceptable and how many resources are appropriate to put toward improving safety and reliability. It isn’t always an easy call but studying past accidents and learning from them can help provide some guidance.

Additional Root Cause Analysis Resources:

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What Can We Learn From Train Derailments?

The train derailment in East Palestine, Ohio, and the response to this disaster displayed many of the challenges and issues that CNA has observed while facilitating rail exercises and analyzing previous train accident responses. These include lack of coordination between the public and private sector, difficulties with preparing for the dual challenges of a train derailment and hazardous material spill, lack of coordination between local governments and train companies, and ensuring that evacuations are handled equitably. Learning new lessons from this derailment will require a high-level analysis of gaps in coordination, planning, and implementation. Developing solutions to those gaps will protect communities alongside tracks nationwide from becoming the next East Palestine.

On February 3, 2023, East Palestine, Ohio, became the epicenter of national attention when a 151-car freight train derailed in this small community. In total, 38 railcars derailed and ignited in flames, damaging 12 cars . Further aggravating the incident for emergency responders, 11 of the derailed cars carried hazardous carcinogenic material , including vinyl chloride and ethylhexyl acrylate. These hazardous materials leaked near open flames, escalating the incident into a multi-day emergency evacuation for 2,000 residents (or 40% of the total population ) out of fear of toxic fumes and shrapnel. Emergency responders decided to vent and burn the toxic vinyl chloride in a controlled setting to mitigate the danger. Unfortunately, the controlled burn resulted in a toxic chemical plume forming over the region and traveling downstream, approximately 265 miles, into West Virginia .

Train Derailment Background

Statistically, travel via railroad is significantly safer than automobile or watercraft , however, train derailments are not rare within the United States. Per the Bureau of Transportation Statistics, there have been 14,151 passenger and industry freight train derailment accidents between 2011 to 2021 . The U.S. Department of Transportation (DOT) noted that between the years of  2015 to 2019, there were 4.8 derailments for every 100 miles of train track . The US DOT further cited the failure of old and broken rails and welds being the most common cause of train derailments in that span of time.

While hazardous spills from train derailments are generally uncommon as compared to other modes of transport, impacts from these disasters are much more significant. In 2022, 18 train derailments involving the release of hazardous chemicals resulted in $41.6 million worth of damages, as compared to $2.1 million worth of damages from 300 chemical leaks in other modes of transportation. In addition to the loss of product and property damage from a train derailment chemical spill, contamination tends to be more widespread throughout the community. Within the past decade, at least 24 recorded evacuation orders were implemented by local governments – or once every seven train accidents. Even when allowed to return to their homes, residents of the affected area suffer lasting effects ranging from long-term health issues to livestock and water contamination. Contamination-related fatalities, though rare, have also been reported in the aftermath of train derailment chemical spills. Between 1994 to 2005, the Federal Railroad Administration reported that 14 people died due to hazardous material exposure. The potential for regional contamination and death is especially concerning as many of our nation’s tracks run through urban populations, and while recent train derailment chemical spills in Houston, Texas, and Detroit, Michigan proved minor, the threat of having a large-scale spill with short- and long-term impacts is daunting.

Ongoing Challenges

CNA has served as the creators and facilitators for local, state, and federal level rail exercises, as well as supporting real-world train accident responses. As a result of these experiences, CNA can note common challenges and lessons learned, many of which are relevant to the events in East Palestine.

Communication gaps between public and private sector

The lasting impacts left in the aftermath of a train derailment chemical spill have sparked debates regarding transparency between government entities and private railroad companies, as called out most recently by Ohio Governor Mike DeWine. In East Palestine, OH, the chemical cargo was not designated as a highly hazardous material, so Norfolk Southern was not required to notify state officials of railcar contents. In fact, no one organization, including the U.S. Department of Transportation, monitors the real-time movement of hazardous chemicals nationwide , and jurisdictions rely on freight companies to be transparent and communicate potential threats.

Planning gaps for local hazardous material response

Multiple jurisdictions discovered via exercise or real-world response that their plans insufficiently address the simultaneous occurrence of a train derailment and a hazardous material spill. Plans were often too broad in scope resulting in confusion and uncertainty in the number of resources, and the types of resources, needed to respond to a train derailment chemical spill. One after action noted that the lack of precision planning resulted in the deployment of various unnecessary resources into a hazardous site resulting in significant delays in response activities. Additionally, implementation of both broad and sufficiently narrow plans has served as gaps in past performances. In each after action CNA has reviewed, the response has fallen short in the implementation and understanding of procedures in one or more plans. The most consistently noted planning gap identified in each real-world event and exercise was the decision making of various senior leadership positions regarding actions that diverged from, or contradicted, jurisdictional Continuity of Operations Plans (COOP) due to a lack of understanding of the document’s role in emergency situations, or its procedures.

Lack of plan sharing and coordination

Another key planning gap identified was the lack of plan sharing amongst critical stakeholders. A critical finding in one exercise noted that while jurisdictions can request a railroad company’s Disaster Response Plan through a formal process prior to an incident, they cannot do so during an ongoing disaster. Therefore, jurisdictions with hazmat-carrying rail traffic should secure relevant rail company Disaster Response Plans prior to an event so local responders can foster greater coordination and understand what roles and responsibilities each response entity has. Joint training and exercise between private rail transport companies and local first responders would also serve to ensure everyone is familiar with and practiced in executing those plans.

Planning for evacuation and shelter-in-pace for vulnerable populations

In mass evacuations, like the one in East Palestine, special consideration and resources must be focused on populations such as the elderly, disabled, hospitalized, and those without a private mode of transportation. These groups will need transportation to a safe area and aid once relocated. Additionally, a key learning point one jurisdiction garnered from an after action was that not all access and functional needs persons are located in set areas such as hospitals, retirement homes, or other controlled environments; many are spread throughout the community and will all need transportation and unique accommodations. Furthermore, vulnerable populations that shelter in place often lack enough food, water, medication, and viable shelter, for long-term hazards. Responders in a chemical spill event should consider the ability to provide necessary life-sustaining functions for these populations and provide adequate shelter for homeless populations through potential power outages, supply chain disruptions, or various other continuity of operations disturbances. Finally, CNA noted that equitability for evacuations and shelter-in-place operations must include translations of government mandates for those with limited or no English.

Out of this tragedy, there is an opportunity to learn still more lessons to help prevent dangerous derailments and improve the response to them. As the East Palestine, Ohio, train derailment chemical spill concludes its recovery efforts in the following weeks and months, response organizations should conduct a high-level overview of gaps in interagency coordination, plan creation and implementation, and vulnerable population action planning for further advancements in emergency response efforts in complex train derailment incidents.

John Kearse is a Research Specialist with CNA's Center for Critical Incident Analysis .

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Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand

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In recent years, with increasing passenger travel demand, high-speed railways have developed rapidly. The stop planning and timetabling problems are the core contents of high-speed railway transport planning and have important practical significance for improving efficiency of passenger travel and railway operation Dong et al. (2020). This study proposes a collaborative optimization approach that can be divided into two phases. In the first phase, a mixed-integer nonlinear programming model is constructed to obtain a stop plan by minimizing the total passenger travel time. The constraints of passenger origin-destination (OD) demand, train capacity, and stop frequency are considered in the first phase. In the second phase, the train timetable is optimized after the stop plan is obtained. A multiobjective mixed-integer linear optimization model is formulated by minimizing the total train travel time and the deviation between the expected and actual departure times from the origin station for all trains. Multiple types of trains and more refined headways are considered in the timetabling model. Finally, the approach is applied to China’s high-speed railway, and the GUROBI optimizer is used to solve the models in the above two stages. By analyzing the results, the total passenger travel time and train travel time decreased by 2.81% and 3.34% respectively. The proposed method generates a more efficient solution for the railway system.

Citation: Li Y, Han B, Zhao P, Yang R (2023) Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand. PLoS ONE 18(4): e0284747.

Editor: Vincent Yu, National Taiwan University of Science and Technology, TAIWAN

Received: April 21, 2022; Accepted: April 7, 2023; Published: April 21, 2023

Copyright: © 2023 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This research was supported by the Fundamental Research Funds for the Central Universities (CN) (Award Number: 2020JBZD007), the National Natural Science Foundation of China (Award Number: 71971019) and the Project Under the Guidance of Cangzhou Key Research and Development Plan (Award Number: 204102012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


High-speed railway has developed rapidly all over the world and won widespread support and popularity with the public for its many significant advantages, including punctuality, comfort, safety, and speed. At the same time, greater requirements are being placed on the transport plans of high-speed railways. Passenger demand grows at a steady rate and is often imbalanced, with extremely varied spatial mobility patterns and flow volumes. Moreover, passengers prefer to use direct services for modes with poor interchange conditions. Therefore, an efficient and scientific transport plan is essential to provide a high level of service that meets the needs of both passengers and operators.

In general, the process of high-speed railway system train planning is divided into three levels, strategic, tactical, and operational, and at the same time, the process is decomposed into network planning, line planning, train timetabling, rolling stock scheduling, crew scheduling, and real-time management [ 1 ], as shown in Fig 1 . As an important part of a transport plan, the train stop plan and timetable contain key elements such as the train stop pattern and train arrival, departure and pass times at stations. In this process, train operating schedules need to be adjusted when passenger demand changes. Once a prespecified stop plan is changed, a new timetable must be generated to accommodate the new train stop requirements. It is clear that this adjustment process can inherently complicate rail operations. Therefore, the design of effective methods to collaboratively optimize train stop plans and train timetables to better meet passenger demand is an issue that needs to be studied.


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Literature review

In recent years, scholars have performed a great deal of research on stop planning, timetabling and the integration of both. In this section, we review the models and methods of the three problems mentioned above.

Stop planning.

Line planning requires specifying the number of trains, train types and their stop plans. Given the number of trains and operation parameters related to train types, it is worth studying to determine the stop plan for each train to fulfill passenger needs with the lowest possible cost. Bussieck et al. [ 2 ] introduced a mixed-integer linear programming formulation to maximize the number of direct travelers. A cutting plane approach was proposed to solve this problem. Claessens et al. [ 3 ] and Goossens et al. [ 4 ] solved the line planning problem by minimizing train operating costs. A further development called the multiline planning problem was proposed by Goossens et al. [ 5 ] later. They provided train lines with different halting patterns to solve the line planning problem. To minimize both passenger travel time and operation cost, Borndörfer et al. [ 6 ] developed a multi-objective optimization model and presented a column-generation algorithm to obtain its solution. Based on the classification of stations and trains, Fu et al. [ 7 , 8 ] proposed a two-stage train stopping optimization method which optimized higher-classification and lower-classification train stop plans respectively. Hu et al. [ 9 ] performed a circuity analyses of China’s high-speed rail network and found a series of reasonable shortest paths for trains. Parbo et al. [ 10 ] presented a bi-level model and a heuristic method to optimize stop patterns in the large-scale network. The lower level is a passenger assignment model, and the upper is the skip-stop optimization model. The results show that the proposed solution have a better performance in reducing passenger travel time. In summary, there are lots of studies to optimize stop plans with the given alternatives, and the specified or variable number of trains.


Generally, the obtained train stop plan is used as partial input to optimize the timetable, which specifies arrival and departure times of trains at all stations for passengers. Among relevant studies, timetables for railways can be divided into two categories: periodic and aperiodic timetables.

In view of a periodic timetable, Serafini and Ukovich [ 11 ] first proposed the periodic event scheduling problem (PESP). Some researchers focused on solving the PESP and then applying it to scheduling [ 12 , 13 ]. Kroon et al. [ 14 ] proposed a variable trip time model based on the PESP. Liebchen et al. [ 15 , 16 ] proposed an integer programming model based on graph theory to optimize the periodic timetable. The approach was applied to the Berlin metro to obtain a timetable with lower train operating costs and passenger waiting time. Many researchers have conducted studies on periodic timetabling considering robustness and stability [ 17 , 18 ]. Goverde et al. [ 19 ] used a max-plus model to perform real-time sensitivity and robustness analyses of large-scale periodic railway timetables. Yan et al. [ 20 ] proposed a railway timetable optimization model with overtaking and variable dwell and running times, aiming to improve the robustness. Sparing et al. [ 21 ] extended the PESP to a variable cycle time formulation and constructed a railway timetable optimization model to maximize railway network stability with flexible train orders, overtaking, and running times.

For aperiodic timetables, many integer programming models have been proposed and solved by various strategies, including the branch-and-bound method [ 22 ], Lagrangian relaxation [ 23 ] and other hybrid algorithms [ 24 ]. Caprara et al. [ 25 , 26 ] proposed a timetabling model based on graph theory and considered additional constraints in real-world applications, which is solved by a Lagrangian heuristic algorithm. Given section running time and dwell time, Niu et al. [ 27 ] developed a nonlinear optimization model containing binary variables indicating departure events and passenger loading to solve the timetabling problem in heavily congested urban rail corridors. However, the skip-stop strategy is not taken into account. To meet dynamic passenger demand, Barrena et al. [ 28 , 29 ] developed three linear optimization models to determine train departure times at stations and running speeds during sections. These aimed to minimize the average passenger waiting time. A fast adaptive large neighborhood search (ALNS) metaheuristic was introduced to solve large instances of the problem. D’Acierno et al. [ 30 ] studied the relationship between dwell times and passenger flow in the metro system. By providing a more accurate estimation of dwell times, the timetable could present a better robustness. Given dwell times and section running times, Niu et al. [ 31 ] developed a nonlinear optimization model to jointly synchronize train service times and effective passenger loading time windows at each station. They considered time-varying demand and found that train trajectories in the final schedule were closely synchronized with them. Robenek et al. [ 32 ] considered both periodic and aperiodic timetabling problem, with the aim of maintaining passenger satisfaction while maximizing the profit of train operators. The results show that the aperiodic timetable performs better for high density demand.

Integrated approach. In recent years, some scholars have performed research on integrated stop planning and timetabling approaches. To generate train stop plan and timetable simultaneously, Yang et al. [ 33 ] proposed a multiobjective integer linear programming model with the aim of minimizing the total dwell time and delay of train service times. To address the problem of optimizing high-speed railway stopping patterns and timetables, Yue et al. [ 34 ] constructed a linear programming model using the Lagrangian relaxation technology, intending to minimize train profit. A heuristic algorithm based on column generation was proposed to solve the problem in a real-world railway. An improved solution was obtained that increases the number of trains and profits. Yan et al. [ 35 ] optimized line plan and timetable by the two-stage approach. The first stage is used to obtain the optimal line plan, which is as the input for the second to optimize multi-period timetable with the aim of minimizing travel time, maximizing timetable robustness and minimizing number of overtakings. These two models works iteratively based on the designed feedback constraints. Considering the dynamic choice behavior of passengers, train service patterns and detailed timetables, infrastructure and rolling stock capacity, Meng et al. [ 36 ] constructed an integrated optimization model to determine train stops and timetable using a Lagrangian relaxation solution framework. Hao et al. [ 37 ] proposed an integer linear programming model for selecting an optimal minimum interval time, which takes symmetrical transport demand, train stopping schemes and train schedules into account. Dong et al. [ 38 ] constructed an integer nonlinear programming model to optimize train stop plan and timetable for commuter railway, without considering overtaking, which is solved by extended adaptive large-scale neighborhood search algorithm. The results showed that the proposed method can improve passenger travel efficiency and reduce train running time. Zhang et al. [ 39 ] considered different numbers of discrete time intervals as passengers’ expected departure time interval, and formulated an integer linear programming model to find the optimal stop plan and timetable with the aim of minimizing operation cost and train travel time.

Table 1 shows recent studies on stop planning and timetabling. According to the above literature review, a lot of research has been carried out on the stop plan, timetable and combination optimization. In summary, it is found that few research takes the relationship between train speed class and stop plan into account. For high-speed railways, there are often different types of trains in operation, which have a great impact on the travel of passengers. Although the headway constraint is included while timetabling, headways are not classified finely according the status of two consecutive trains. Variable dwell times and number of stop stations are considered in most studies while optimizing train timetable, but the time loss caused by acceleration and deceleration at the station is rarely taken into account. We also note that although some studies take minimizing the train delay as the objective. Each train is assumed to depart from the origin station not earlier than its expected departure time, which is too restrictive and inconsistent with the actual situation. Therefore, we need to find an effective and realistic way to combine train stop planning and train timetabling processes into a system-based optimization strategy where more complex situations and parameters need to be taken into account.


Proposed methods

This study aims to provide the following contributions to the framework of train planning methods.

  • A collaborative optimization approach for stop plans and timetables is proposed in this paper. Two mathematical models are formulated in two phases with consideration of multiple train type. In the first phase, a mixed-integer nonlinear programming model is constructed to obtain a stop plan to minimize the total passenger travel time. In the second phase, the train timetable is optimized based on the stop plan obtained in the first phase. A multiobjective mixed-integer linear optimization model is formulated to minimize the train travel time and the total deviation between the expected and actual departure times from the origin station for all trains.
  • In the process of developing the models, we linked passenger OD demand to train stop planning in the first phase. A decision variable is used to represent the OD passenger flow specific to each train. A train stop planning model is constructed based on the passenger flow assignment. Once the train stop plan is obtained, the train lines need to be rationalized on the time-distance diagram in the second phase. We adopt binary variables to control the train departure sequence from the origin station. A departure time selection matrix based on multiple types of trains is established to constrain the departure sequence of trains. The headway times under different operating conditions are also taken into account in the model.
  • The proposed approach is applied to a real-world case study on a Chinese railway corridor. We use the operating data of the Beijing-Shanghai high-speed railway as model input and adopt the GUROBI optimizer to solve the proposed model. According to the computational results, our models and method generate a more efficient solution for the transport system within an acceptable computation time.

The remainder of this paper is structured as follows. Section 2 presents the problem statements and makes some assumptions. Section 3 gives details of the models in the collaborative optimization approach. In Section 4, the proposed approach is applied to a real case, and the results are analyzed to demonstrate the effectiveness of the models. Some conclusions and future research work are given in Section 5.

Problem statements and assumptions

As two important parts of a transport plan, a better stop plan and timetable not only meet the passenger demand and provide high-quality transport services but also achieve efficient use of transport resources and reduced operating costs. High-speed railway train stop planning is prepared to determine the stop sequence of trains according to passenger demand, train operation conditions, and station service content. The main task of timetabling is to schedule the departure and arrival times of each train at each station.

Therefore, an optimization approach needs to be constructed from three levels of the network, as shown in Fig 2 . The first level is the railway infrastructure network, which consists of stations and tracks. The second level is the set of train lines with different stop patterns, which provides travel services for passengers of each OD. Once the stop plan is scheduled, time attributes to each line must be added in the third level. The third level is the train timetable, which consists of the departure and arrival times of each train at each station. A simple network with three trains and six stations is also shown in Fig 2 , in which subplots a, b, and c represent the rail infrastructure, stop plan, and timetable, respectively. Three trains operate between the same terminal stations in this railway corridor. The first train with the high-speed class (class A ) stops only once at station C. The second and third trains are both low-speed class trains (class B ). The second train stops at stations B and D, while the third train serves all stations.


Clearly, these three trains can provide service for passengers between all ODs. The total travel time of different train services will influence the passenger travel choice with the same OD. For example, all three trains provide transport service from station A to F. Because of the higher speed and fewer stops, the first train is more attractive for passengers from station A to F. When the first train is full, passengers can only choose the last two trains. With the same speed, the travel time of both trains is only affected by the train stop plan. The third train makes two more stops than the second train at stations C and E. More stops increase dwell time, and the corresponding additional acceleration and deceleration time cannot be ignored, especially for high-speed railways. The remaining passengers from station A to F will give priority to the second train with less travel time. Therefore, it is necessary to develop an optimization model that considers passenger assignment and the train stop plan.

Once a train stop plan is generated, the set of train lines needs to be properly aligned on a time-distance diagram. As shown in Fig 2C , there are two timetables obtained from the same stop plan with the different departure orders of the three trains. If the departure sequence from the origin station is train 3—train 1—train 2, train 1 should overtake train 3 at station E to avoid conflict in the section. The dwell time will increase at this point, leading to an extension of the total travel time. There are two strategies to avoid this problem. The first strategy is to adjust the departure sequence of the trains. If the departure sequence from the origin station is train 2—train 1—train 3, there is no overtaking in the timetable, as shown in Fig 2C . The second strategy is to adjust the departure time of trains. Advancing the departure time of train 3 or delaying the departure time of train 1 from the origin station can also avoid overtaking. Therefore, a timetabling model needs to be developed to compute a more efficient and practical solution.

The entire optimization process is divided into two phases. In the first phase, the main objective of a stop planning model is to minimize the total travel time of passengers. From the passenger perspective, the passengers of the same OD may have different train services with different travel times which can affect the selection of train lines during a trip. Travel services with the shortest travel times are more attractive to passengers. The shortest path for passengers will be found in this optimization process. Thus, the problem of passenger assignment based on the shortest path and capacity constraints is embedded into the stop planning model to obtain a system-optimal solution.

In the second phase, the two main objectives for a timetabling model are to minimize the total train travel time and the deviation between the expected and actual departure time from the origin station for all trains. The train travel time is controlled mainly by reducing the dwell time when the running time in the section is fixed. Usually, the timetable of a high-speed railway is relatively fixed over a period of time, which is convenient for passengers to remember and helps to simplify the daily scheduling of the operator, so it is not advisable to change the structure of the timetable to an excessive extent. Therefore, a multitype train departure selection matrix is created to constrain the departure sequence of trains, which controls fewer changes to the structure of the timetable. For example, if a train with a high-speed class departs at 8 am from the origin station, this is also maintained as far as possible in the optimized result. In addition, a timetabling model is proposed to obtain a more efficient result by considering train operation constraints.

To simplify the modeling process, the following assumptions are made.

  • The high-speed railway line studied in this paper is a two-line railway, and the upstream and downstream systems are completely independent. Only one train operation direction is considered in the modeling process.
  • The capacity of the section and station refers to the number of trains that can be arranged per unit time. The capacity constraints can still be satisfied in this study.
  • The study in this paper is based on a single high-speed railway corridor. We assume that all passengers choose trains that can reach their destinations directly.

Mathematical models

This section will specify each part of the models, including decision variables, parameters, objective functions and systematic constraints. The notations of the parameters and variables are summarized in Table 2 .


The first phase: Stop planning model

A stop planning model needs to be formulated in the first phase.

Objective function.

The train stop planning model takes the minimum total travel time of passengers as the optimization objective. The total travel time of passengers includes the waiting time at the station and the in-vehicle time. Generally, passengers traveling on high-speed railways buy their tickets in advance and are given departure times. Depending on their travel arrangements, they arrive at the station before the train leaves to make sure they can board the train. Therefore, the waiting time of passengers at the station is not considered, and we only consider the in-vehicle time, which can be formulated as Eq ( 1 ). The travel time of the train includes pure running time, dwell time, and additional acceleration and deceleration time, which can also be formulated as Eq ( 2 ).

case study on train

Passenger flow constraints.

The passenger flow of each OD must be served. Constraint (3) specifies that the sum of passengers assigned to each train is equal to each passenger OD demand. Constraint (4) indicates that if a train does not provide service between two stations, there is no passenger flow in this train between this OD. U is a large positive number. Constraint (5) specifies that the passenger flow on each train is a nonnegative integer.

case study on train

Seat capacity constraints.

Constraint (6) ensures that the passenger flow on each consecutive section of each train is no more than the train seat capacity.

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Station service frequency constraints.

Different classes of stations require different train service frequencies to meet passenger demand. Constraint (7) limits the number of train stops at a specific station to a reasonable range.

case study on train

Number of train stops constraints.

In actual operation, there are different classes of trains on a high-speed railway to meet the travel demand of various passengers. Higher class trains have fewer stops and higher speed, providing faster service for long-distance passengers; while lower class trains have more stops and slower speed, providing service for more passengers of different OD. Passengers can choose the appropriate train service according to their needs. Constraint (8) controls the number of stops of each train within a reasonable range.

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The second phase: The timetabling model

When the stop plan is determined, a timetabling model needs to be formulated in the second phase.

Objective function. The train timetable model is a multiobjective model. The first objective is to minimize the total train travel time, and the second objective is to minimize the total deviation of the train departure time from the origin station, which can be formulated as Eqs ( 9 ) and ( 10 ).

case study on train

Train departure time selection constraints. It is important to set the departure time of each train from the origin station. A set of expected departure times can be obtained from the original timetable. Constraints (11)–(13) assign a unique departure time to each train from the origin station. There are many types of trains operating on high-speed railways. Some high-speed class trains with few stops usually have a regular departure time, such as every hour. Moreover, high-speed railway train timetables are usually fixed in a period of time to facilitate the memory of passengers and daily operation management. To reduce changes in the structure of an optimized train timetable, some limitations on the departure times of certain trains are needed. Therefore, a select limitation matrix denoted as Zmatrix ij is proposed to limit the selection of departure times for each train. All trains are divided into several types according to the terminal stations and operating speed, and each type corresponds to a set of expected departure times. The optimized train departure times from the origin station can only be selected from the set of departure times corresponding to the type to which they belong. Constraint (14) limits the selection of departure times for each train.

case study on train

Interstation travel time constraints.

The arrival and departure times of a train at each station can be calculated using Eq ( 16 ). The travel time between two stations includes the pure running time and additional acceleration and deceleration time according to the stop.

case study on train

Dwelling time constraints.

Constraint (17) ensures that the dwell time at a station is greater than a lower bound.

case study on train

Train operation headway constraints.

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Train operation headway calculation.

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Departure time from origin station constraints.

Constraints (11)-(14) assign an expected departure time to each train. However, it may not be feasible to design a timetable strictly according to the expected departure time. Therefore, we allow the train departure time to fluctuate within a deviation Δ t , shown as Constraint (24).

case study on train

Computational experiments

In this section, the Beijing–Shanghai high-speed railway in China is considered to verify our approach. Fig 6 shows the Beijing-Shanghai high-speed railway corridor, which is from Beijing South to Shanghai Hongqiao with a total length of 1318 km. This high-speed railway includes 23 stations, of which Beijing South, Tianjin West, Jinan West, Xuzhou East, Nanjing South and Shanghai Hongqiao are the main terminal stations.


Data preparation and parameter setting

Before optimization, some basic data need to be input, including train operation data, passenger OD demand, and other model parameters.

In this case, we use the train timetable and passenger OD demand from 7:00 to 13:00 on a weekday in 2018 shown in S1 and S2 Tables. The train operation information is shown in Table 5 , including the train number, train class, terminal stations and departure times from the origin stations. All trains can be divided into six types according to the terminal stations and operation speed, as shown in Table 6 .



Table 7 shows the pure running time of the two train classes in each section. Due to the speed limit in some railway sections, the calculation of train travel time according to distance and speed is difficult. To simplify the model, the train travel time in a section is obtained from the existing train timetable, which includes pure running time and additional acceleration and deceleration time.


Other parameters in the model are shown in Table 8 .


Results and analysis

In this case, a better service plan is obtained using the proposed collaborative optimization method. We use Python 3.7 and GUROBI ( ) version 9.1.2 to solve the models with the given data and parameters. GUROBI is a powerful mathematical programming solver available for optimization problems. GUROBI Optimizer enables users to state their problems as mathematical models and then finds the best solution. For mixed integer programming problems, GUROBI uses the branch and cut algorithm to solve them exactly. The results and analysis of each phase are shown below.

The first phase

From the given train information, the passenger OD demand, train seat capacity, and a more efficient stop plan are generated with a gap of 4.13% after 15000 s. Fig 7 shows the specific train stop plan in which a solid dot “•” indicates the stations a train services.


case study on train

where dis i and dis m , n represent the total travel distance of train i and the distance between stations m and n , respectively. The total passenger travel time has been optimized from the original 6,694,631 minutes to 6,506,499 minutes, which is a reduction of 2.81%. There are 226 stops in the optimized plan to provide transport services for all passenger ODs, which is 15 fewer than the original plan. Clearly, the optimized average seat occupancy is increased by 4.62%. It can be seen that the optimized stop plan is more efficient while still meeting the passenger demand. Fewer stops will also result in lower operating costs for the operator. Moreover, train seating resources are utilized more efficiently in the optimized solution.


case study on train

Changes in the frequency of stops at each station and the connection with passenger flow is depicted in Fig 8 . This demonstrates that the frequency of stops varies with the flow of passengers. The frequency of service has changed at most stations. The frequency of stops at large-sized and medium-sized stations has mostly been reduced since the passenger flow is relatively low and can be met without many stops. Conversely, the frequency of service at some small-sized stations has increased because the low but not concentrated passenger flow needs more services to be satisfied. Therefore, the optimized stopping plan is more in line with the real situation.


The second phase

Based on the generated stop plan, train operation parameters and expected departure times, a new timetable is obtained from the proposed timetabling model by minimizing the train travel time and departure time deviation. We set w 1 and w 2 as the weights of Z 1 (train travel time) and Z 2 (departure time deviation) respectively. In this experiment, w 1 = 20 and w 2 = 1. The result is obtained after 3600 s and the gap is 0.44%. Fig 9 shows the results of the test, where the different colored lines indicate different types of trains.


Table 11 reports a comparison of the total train travel time, average dwell time and number of overtakings between the original and optimized timetables. The total train travel time has been optimized from the original 8,362 minutes to 8,080 minutes, which is a reduction of 3.34%. The average dwell time is also optimized from 3.09 minutes to 2.19 minutes. The number of overtakings is reduced by 5 times. The results indicate that the proposed model generates a better solution in actual operation.


Table 12 gives the actual and expected departure times of each train from the origin station. The total departure time deviation are 69 minutes. The departure times of most trains are adjusted within 10 minutes, in which 9 trains maintained their departure times after optimization.


The weight of the total train travel time in the presented model has an effect on the actual departure time in the solution. Finally, we performed some sensitivity analysis to compare the impact of the two objective in timetabling model. A series of experiments using the data in the second phase is proposed, in which w 1 is different and w 2 is fixed to 1. The computation time is set to 3600s. Table 13 shows the experimental results. The contrasts of train travel time and departure time deviation is shown in Fig 10 .



In Fig 10 , with increasing w 1 , the departure time deviation becomes larger and train travel time becomes shorter. When w 1 is large, it is necessary to reduce the limit of train departure time to meet the shorter travel time, so the departure time deviation will become larger. Hence, train travel time and departure time deviation can be balanced by adjusting the weight of the objective.


Aiming at the problem of train stop planning and timetabling, this study proposes a collaborative optimization approach that contains two phases. A stop planning model was constructed by minimizing the total passenger travel time in the first phase. The constraints of OD passenger flow, train seat capacity and number of stops were considered in the stop planning model. In the second phase, a timetabling model was formulated with two objectives, which were to minimize the total train travel time and the total deviation between the expected and actual departure times from the origin station for all trains. We considered a set of train operation constraints to ensure safe train operation, while departure time selection constraints based on multiple types of trains are proposed to control the changes in the optimized timetable. According to a real-world case study, the proposed approach generated a train plan that is more efficient for both passengers and operators.

In future research, the following three areas can be seen as priorities. (1) Unfixed train numbers need to be considered in the model. We need to add or remove trains to accommodate significant changes in passenger flow, such as during holidays. (2) It is meaningful to integrate the two-phase problem into a single model with multiple objectives. Then multiple non-dominated solutions will be obtained for decision-making. (3) The efficient heuristic algorithms can be developed to solve the model, as the efficiency of GUROBI Optimizer is relatively low in solving larger-scale real-world problems.

Supporting information

S1 table. original timetable..

S2 Table. Passenger OD demand.

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Noise pollution in rail transport. Case study: Baghdad subway

Noise pollution is one of the environmental factors that severely threaten humans’ mental, emotional, and physical health. One of the most significant sources of this pollution is the noise generated by traffic and urban transportation, mainly the urban railway. This paper assessed noise pollution at stations, passenger cars, and the train operator’s cabin. Four consecutive Baghdad subway stations were used to collect information. There were three modes of measurement: entering the station, stopping at the station, and leaving the station. L eq (equivalent continuous noise level) at station 3 is more significant than at three other stations, confirming that the initial hypothesis regarding the noise pollution rate was correct. Among the studied stations, Station 1 was the quietest. The stations with the highest and lowest L eq values are 3 and 2, respectively. The L eq values recorded in the cabin are within the permissible range. The highest L eq value measured at station 3 is 81.87 dB(A), and the lowest L eq value measured at station 2 is 61.24 dB(A). The operator’s cabin at station 3 has the highest measured L eq of 70.26 dB(A), and station 1 has the lowest measured L eq of 61.5 dB(A). While the measured value in the operator’s cabin was within the acceptable range, the noise levels in the wagons were above standard.

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Noise annoyance through railway traffic - a case study

Paulo henrique trombetta zannin.

1 Laboratory of Environmental and Industrial Acoustics and Acoustic Comfort, Federal University of Paraná, Curitiba, Paraná, Brazil

Fernando Bunn

This paper describes an assessment of noise caused by railway traffic in a large Latin American city. Measurements were taken of noise levels generated by trains passing through residential neighborhoods with and without blowing their horns. Noise maps were also calculated showing noise pollution generated by the train traffic. In addition - annoyance of the residents - affected by railway noise, was evaluated based on interviews. The measurements indicated that the noise levels generated by the passage of the train with its horn blowing are extremely high, clearly exceeding the daytime limits of equivalent sound pressure level - L eq = 55 dB(A) - established by the municipal laws No 10.625 of the city of Curitiba. The L eq = 45 dB (A) which is the limit for the night period also are exceeded during the passage of trains. The residents reported feeling affected by the noise generated by passing trains, which causes irritability, headaches, poor concentration and insomnia, and 88% of them claimed that nocturnal noise pollution is the most distressing. This study showed that the vast majority of residents surveyed, (69%) believe that the noise of the train can devalue their property.


Noise pollution today is no longer restricted to industrial environments but affects small, medium and large cities all over the world. It is a daily reality both in developed countries such as the United States and the European nations and in emerging countries such as India, China and Brazil.

Many sectors of society are affected by noise, particular which is generated by traffic. Traffic noise – road , air , and railway – causes discomfort and irritation, especially during activities that require attention and concentration [ 1 - 14 ].

Traffic noise is also a serious source of annoyance for people trying to rest and relax at home [ 15 - 18 ], particularly when it interferes with sleep, which is indispensable to human health, contributing to the degradation of quality-of-life [ 19 - 23 ].

Noise pollution in urban environments comes from numerous sources, e.g., sirens, loud music, neighbors, car and home alarms, religious temples, horns, motorcycles, trucks, passenger cars, buses, planes, trains, etc. [ 24 - 27 ].

Brazil’s rail network currently covers approximately 30,000 kilometers, and accounts for over 20% of the country’s freight transport [ 28 ]. Figure  1 compares the extent of the Brazilian rail network to that of other countries.

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Extent of rail networks in several countries (Adapted from [ [ 29 ] ]).

The Brazilian rail network is used primarily for transporting bulk commodities, such as soybeans, from the country’s producing regions to its shipping ports for export. The port of Paranaguá, situated in the state of Paraná in southern Brazil, is one of the main export outlets for the country’s agricultural production. In 2009, this shipping port handled 31.3 million tons of freight, of which approximately 8.6 million tons were transported by rail [ 30 ].

The railway line linking the producer regions in the interior of the state of Paraná to this shipping port was built in the late 19 th century. On its route to the shipping port the railway line passes through Curitiba, the capital of the state of Paraná. The 319-year-old city of Curitiba is one of the oldest in Brazil, with a population of approximately 1.8 million. The stretch of railway line that runs through the city covers about 20 km. Figure  2 shows part of the route of the railway line through Curitiba.

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A portion of the route covered by the railway line in the city of Curitiba. Areas affected by train noise: Residential areas, Hospital and Public Park, Residential area under construction (five 12-story buildings).

On its route through the city, the railway line crosses urban thoroughfares and passes through residential neighborhoods. As a safety measure, trains blow their horn before they reach a railroad crossing (see Figure  3 ). However, there are no barriers that close automatically to prevent the passage of vehicles, and fatal accidents are not infrequent. Figure  4 shows a typical railroad crossing without barriers in Curitiba, unlike Germany, for instance, where they typically exist. Figure  5 shows safety signs drawing attention to railroad crossings. The trains pass through 40 crossings and blow their horn at least three times as they approach a crossing, thus blowing their horns at least 120 times as they pass through the city. Since an average of ten trains pass through the city each day, their horns are blown at least 1200 times per day.

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Photograph of a train horn mounted on the roof of the locomotive.

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Urban street railroad crossing without safety barriers.

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Safety signs warning of urban street railway crossings. Left: RAILWAY/CROSSING; Right: warning sign, from top to bottom: STOP/LOOK/LISTEN.

The railway noise is a serious environmental problem, as reported in the lengthy study by Fields and Walker [ 22 ]. These authors evaluated the response to railway noise in residential areas in Great Britain, and reached the following conclusion: “ Noise is rated as the most serious environmental nuisance caused by railways .” The literature on environmental noise pollution contains several reports on railway noise in different countries, including the United Kingdom, France, Japan, Sweden, the Netherlands, the United States of America, Switzerland, and Germany [ 16 , 22 , 31 - 36 ]. In Brazil, however, studies about railway noise are as rare as to be practically nonexistent, with a very exceptions such as the works of Bertolli and de Paiva [ 37 ] and Roland and Zannin [ 38 ].

This paper describes an assessment of the annoyance caused by railway noise in a large Latin American city, based on noise measurements, noise mapping, and interviews.

Materials and methods

The environmental impact generated by railway noise in the city of Curitiba was characterized based on several parameters: 1) noise level measurements at railroad crossings with the train horn blowing; 2) noise level measurements at railroad crossings without the train horn blowing; 3) noise maps showing the situation of noise pollution generated by train horn blowing; 4) noise maps without train horn blowing; 5) noise measurement at the receiver, i.e., inside the home of a resident in a neighborhood affected by railroad noise; and 6) interviews with the population of a district through which the railway runs.

The noise levels – equivalent sound pressure levels, L eq - were measured according to the Brazilian standard for noise assessment in urban environments, NBR 10151 [ 39 ], at various points along the railway line. In addition to the L eq , the maximum and minimum noise levels were measured. A Brüel and Kjaer 4231 sound calibrator and five Type 1 integrating sound pressure level (SPL) meters (Brüel and Kjaer B&K 2270, B&K 2260 (two of this model), B&K 2250 and B&K 2238) were used for the noise measurements.

Advances in computational resources have led to the development of several software programs for analyzing environmental noise pollution [ 40 ]. The SoundPLAN Version 6.2 software package was used in this study for the calculations involved in noise mapping to evaluate the noise levels caused by the railway. The current literature contains several studies which used noise mapping as a tool for environmental impact assessment (see, for instance [ 41 - 44 ].

The German prediction method for railway noise, Schall 03, was used to calculate the noise generated by trains [ 34 , 45 ]. In this method, the Mean Emission Level – MEL can be calculated in two ways: 1) From the data flow, and 2) From data entered directly into the software, e.g., noise measurements [ 45 ]. In this study, noise mapping was performed by entering the measured noise levels as input data in the software. After entering this data, specific corrections must be made for the MEL, considering, among other factors, type of track, bridges, and railroad crossings.

To simulate the noise levels emitted by train horns, measurements were taken in situ , to enter them as input data into the software. After entering the railway data into the software SoundPlan, an area of calculation must be chosen with a given certain grid (average number of calculation points). For an environment that is little urbanized, a grid spacing of 20 to 50 meters suffices for acoustic mapping. However, for a highly urbanized region, the handbook of the software SoundPlan, indicate that the grid spacing may vary from 5 to 15 meters. The grid adopted in this work was 5x5 meters in order to produce a higher level of detail of the noise levels on the acoustic map. The height of the grid used in the calculation, as well as by other authors was 4 meters.

The simulated data were calibrated by placing a receiver point at the site where each noise measurement was taken. Measured and simulated levels were compared at the same height, in this specific case, 1.2 m. The calibration was based on the recommendations of the European Commission Working Group – Assessment of Exposure to Noise [ 46 ], for which the expected uncertainty is 4.6 dB(A) [ 47 ] when measured and simulated values are compared.

The steps taken to simulate rail noise are shown in the flow diagram shown in Figure  6 .

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Steps involved in the computer simulations.

To assess the degree of annoyance due to noise generated by the train traffic, interviews were conducted with the residents of neighborhoods that are crossed by the railway line. The researchers handed a questionnaire to each household. One person per household responded to the questionnaire. After two weeks the researchers collected the questionnaires. One hundred and fifty questionnaires were distributed, and 130 were collected. This research was performed according to the Helsinki Declaration.

Results and discussions

The trains passing though the city of Curitiba follow a pattern that is repeated at each railroad crossing. Shortly before reaching each crossing, the train blows its horn three times. Ten railroad crossings were evaluated, and noise measurements were taken at each of them in three different situations: A) Train passing with horn blowing, B) Train passing without horn blowing, and C) Surroundings of the railroad crossing without the presence of the train.

Figure  7 shows an example of a railroad crossing where a set of measurements were taken along the railroad, as described above. Each railroad crossing was assigned a number from 1 to 10, and the three different measurement situations were assigned a subindex (A, B, and C).

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Measurement points along the railway line: A – Train passing with horn blowing, B – Train passing without horn blowing, C – Ambient noise without train passing.

Table  1 describes the noise level measurements at various points along the railway line, for the situations described in Figure  7 .

Noise levels measured along the railroad and surroundings

The simulated data were calibrated by placing a receiver point at the site where each noise measurement was taken. Measured and simulated noise levels were compared at the same height, in this specific case, 1.2 m.

The calibration was based on the recommendations of Licitra and Memoli [ 47 ], whereby it is expected that the difference between the simulated and measured noise level does not exceed the value of 4.6 dB (A).

Based on the above, Table  2 shows the measured noise levels and the noise levels calculated by the software SoundPLAN. As can be seen in Table  2 , last column to the right, the differences between simulated and measured values was below 4.6 dB (A), as recommended by Licitra and Memoli [ 47 ].

Comparison of measured and simulated noise levels

The railroad crossings listed in Table  1 are located in Urban Residential Areas for which Law 10625 of the municipality of Curitiba [ 48 ], which enacts laws about urban noise, establishes that daytime noise levels, from 7:01 a.m. to 7:00 p.m., should not exceed 55 dB(A). Thus, it is evident that the noise generated by passing trains exceeds the limits established by municipal legislation, resulting in noise pollution.

To analyze the noise generated by rail traffic based not only on measurements, SoundPLAN software was used to calculate noise maps for two situations: 1) Train passing with horn blowing, 2) Train passing without horn blowing. The results obtained from these simulations indicate how high the noise levels are. Figure  8 show the noise map in three dimensions, of when a train passes with its horn blowing.

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3D noise map of the situation when the train is blowing its horn.

The map in the above figure show that the passage of trains blowing their horns generates noise levels of 80 to 92 dB(A) at the facades of the homes closest to the railway line. Moreover, they indicate that the noise levels that reach the more distant homes range from 68 to 80 dB(A). Figure  8 also indicates that together with the train, the noise levels at the centerline of the noise map exceed 96 dB(A). The noise maps were calculated based on railroad crossing no. 2 and measurement situation “A,” as indicated in Tables  1 and ​ and2 2 .

Curitiba’s urban legislation [ 48 ] establishes a maximum daytime noise level of 55 dB(A) for the area of this study, which is a residential area. Therefore, the situation is clearly one of noise pollution, since the noise levels generated far exceed the legally established limit. It should be kept in mind, as explained earlier, that trains pass through the city about ten times a day, blowing their horns about 1200 times as they approach the city’s 40 railroad crossings.

The map in Figure  9 , show the scenario when the train does not blow its horn. The noise emission level decreases significantly with the elimination of the blowing horn. The noise levels in the proximities of the rail line vary from 68 to 80 dB(A), in contrast with the situation with the horn blowing, when the levels varied from 80 to 92 dB(A).

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3D noise map of the train passing without blowing its horn.

The noise maps indicate the efficiency of this noise control measure, the elimination of the blowing horn. However, it is also clear that although the noise levels are drastically reduced, they still exceed the noise limits established by municipal legislation.

The analysis of questionnaires filled out by residents indicated that they are well aware of the problem of noise generated by the trains, since the majority, 62%, have lived there for one to five years, and 25% have lived there for over five 5 years. Only 18% of the respondents have lived there for less than a year.

Residents were asked to assess whether – during the time they have lived there – the noise has increased, remained the same or decreased. Among the respondents, 65% indicated that the noise has increased, 33% indicated that the noise has remained the same, and only 2% stated that it has decreased. With regard to the intensity of noise, 57% classified it as very intense, 35% as intense, and 8% as little intense.

When asked if the noise in the neighborhood bothers them, 84% answered YES , 15% answered NO , and 1% did not answer the question. Asked if they believe that environmental noise is harmful to their health, 98% of the residents answered YES and only 2% answered NO .

Residents were asked whether – they find the noise irritating , to which 92% answered YES and 8% NO . Table  3 lists the noises considered sources of irritation to residents that answered YES to the question: “Is this noise source a cause of irritation?”

Noise as a cause of irritation and percent of interviewees affected by it

The residents were asked whether noise leads to – poor concentration , to which 86% said YES , 13% answered NO , and 1% did not respond. Table  4 lists the noise sources that interfere with concentration, for residents who answered YES when asked: “Does this source of noise lead to poor concentration?”

Noise causing poor concentration and percent of interviewees affected by it

Residents were asked whether the noise causes – headache , to which 59% responded YES , 39% answered NO , and 2% did not respond. Table  5 lists the noise sources causing headaches in residents who answered YES to the question: “Does noise give you headaches?”

Noise as a cause of headaches and percent of interviewees affected by it

The residents were asked what time of the day they consider the most bothersome in terms of noise. The great majority, 88%, stated that the most bothersome time is the nighttime. Asked if the noise causes them – insomnia , 73% of the respondents answered YES , and 27% NO .

The respondents who answered YES when asked whether noise caused insomnia were asked to point out the main sources causing insomnia. Table  6 lists the noise sources that cause insomnia in residents who responded YES to the question: “Does noise interfere in your sleep?”

Noise causing insomnia and percentage of respondents affected by it

The interviewees were asked how frequently – their sleep is disrupted by noise , to which 58% answered Often , 32% Sometimes , 9% Never , and 1% did not answer. They were then asked whether – sleep is interrupted by the noise of the train , with 70% claiming that their sleep is Interrupted Frequently , 21% Sometimes 8% Rarely or Never , and 1% did not answer.

Table  7 lists the times of the day when, according to the residents, the noise of the train is the most frequent nuisance.

Time of the day when the train ’ s noise is the most annoying and percentage of respondents affected by it

As reported above, 88% of the interviewees indicated that the noise of the train is the most annoying during the nighttime. In view of this finding, measurements were taken of the nighttime noise generated by passing trains. To this end, a sound level meter was installed in a sound receiving location – the home of a resident. The distance from the railway tracks to the receiver site (the resident’s home) is about 200 meters. As Figure  10 shows, the measurements started before 10 p.m. and ended after 6 a.m. A B&K 2238 sound level meter was used and the measurements were taken with a datalog module (noise levels vs. time of measurement), with measurements recorded at 10 minute intervals.

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Noise levels as a function of the time of day. Nighttime measurements of train noise taken at the home of a resident.

Figure  10 indicates that two trains passed by the measurement location between 10 p.m. and 6 a.m., one at 10:10 p.m. and the other at 6:20 a.m. Both trains blew their horn, as evidenced by the high values of maximum sound level, L max , and equivalent sound level, L eq .

Figure  10 shows how high the noise level is when a train passes with its horn blowing, since the maximum sound levels reached nearly 80 dB(A) at the railroad crossing at 10:10 p.m. and 78 dB(A) at 6:20 a.m. The equivalent sound pressure level reached L eq = 60 dB(A) at 10:10 p.m. and L eq = 58 dB(A) at 6:20 a.m.

Curitiba’s municipal Law 10625, which regulates noise in communities [ 49 ], establishes that the noise levels, L eq , from 10 p.m. to 7 a.m. cannot exceed 45 dB(A) in the region where the nighttime measurements were taken. Therefore, it is a clear violation of this law during the nighttime.

The measurement shown in Figure  10 proves what the residents claimed, as indicated in Table  7 , i.e., the daytime periods from 4 to 6 a.m. and 6 to 8 a.m., and the nighttime period from 10 p.m. to midnight are the periods of greatest annoyance due to train noise. 37% of the respondents stated that the noise between 4 and 6 a.m. was the most annoying, while 43% stated it was between 6 and 8 a.m., and 35% claimed that the noise between 10 p.m. and midnight was the most disruptive.

Lastly, the residents were asked whether they believe that local noise can devalue their home, to which 69% responded YES , 28% NO , and 3% did not answer the question.

Evaluating the effect of aircraft noise on home value depreciation, Espey and Lopez [ 50 ] showed that the value of homes located in areas close to an airport, where noise levels were 65 dB(A) or higher, was about $ 2400 lower than similar homes located in areas not considered noisy. Railway noise also has an impact on the value of homes. The train horn is considered a major cause of high noise levels near railway lines. Bellinger [ 49 ] evaluated the cost of noise generated by blowing train horns in a small town in Pennsylvania. According to him, real estate market values depreciate by 4.1% for every 10 dB above the background noise level. Considering the 256 homes affected, the losses represented a total of about $ 4 million in 2004 market values.


The present study evaluated the noise generated by railway in a large Latin American city. Several analytical techniques were used – measurements of noise levels during the passage of the train with and without its horn blowing, measurement of noise levels in the home of a resident affected by the noise of the train and calculation of noise mapping. Lastly, to assess the degree of annoyance due to noise generated by train, interviews were conducted with the residents of neighborhoods that are crossed by the railway line.

As in the study presented here, research conducted in Poland by Szwarc et al. [ 51 ], and in Germany by Czolbe [ 52 ] also used noise maps to diagnose the impact of noise generated by railway traffic in urban areas.

The measurements indicated that the noise levels generated as the train passes with its horn blowing are extremely high, clearly violating Curitiba’s noise legislation. The noise mappings showed that a simple solution to control noise would be for the trains to pass through the city without blowing their horns. However, although the noise levels are significantly lower when the train’s horn is not blown, they still exceed the levels established by municipal legislation.

The city has been suffering from this problem for decades. The solution to the problem would be to remove the railway line passing through the city. However, lack of resources and of political will are two obstacles to the removal of the trains passing through residential areas within the city.

The residents were found to feel strongly affected by noise generated by passing trains. Train noise causes irritation and annoyance , headache s, poor concentration and insomnia . In terms of noise pollution, 88% of the respondents cited nighttime as the most critical time of the day. As shown in this paper, the research of Fields and Walker [ 22 ] in Great Britain, Lambert et al. [ 16 ] in France, and Ali [ 53 ] in Egypt also show that the population neighboring railways feels disturbed by the noise of the train.

This study showed that the vast majority of residents surveyed (69%), believe that the noise of the train can devalue their property. We would do well to keep in mind the words of Fields and Walker [ 22 ]: “ Noise is rated as the most serious environmental nuisance caused by railways .”

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

Both authors participated in the planning and performance of the measurements and simulations. Both authors contributed equally to the writing of the manuscript. Both authors read and approved the final manuscript.


The authors gratefully acknowledge the German Government, through the German Academic Exchange Service – DAAD (Deutscher Akademischer Austauschdienst) and the Brazilian Government, through the National Council for Scientific and Technological Development – CNPq, for their financial support, which enabled the purchase of the sound level meters and software used in this study. The authors would like to thank the reviewers who contributed to the improvement of this work with their observations.

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Case Study: The Madrid Train Bombing of March 11, 2004

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  • Isaac Ashkenazi 3 , 4 , 5 ,
  • Scott D. Deitchman 6 , 7 &
  • Henry Falk 8  

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Madrid, the capital of Spain, is a highly Westernized metropolis with a well-developed and modern emergency system that has extensive experience responding to terror attacks. Nevertheless, the March 11, 2004 (M-11) train bombings resulted in a mass casualty incident (MCI) that produced a casualty load of 2062 victims, almost immediately overwhelming the medical emergency response system. Local ambulance services and hospitals were severely challenged by the multiple casualties, cadavers, inrush of both families and media representatives, etc. The M-11 train bombing stands as an important marker to prepare for similar catastrophic events and to prevent systemic failures in the response. This case study briefly presents the main lessons learned of this event and provides recommendations for improving emergency system readiness. One of the authors (IA) participated in post-event assessments of the response; the case study includes his personal observations as well as those of published post-incident reviews.

  • Blast injury
  • Blast injury epidemiology
  • Improvised explosive device (IED)
  • Civilian blast injury
  • Military blast injury
  • Explosive remnants of war (ERW)
  • Mass casualty incident (MCI)
  • Preparedness
  • Active bystanders
  • First responders
  • Flow of casualty care
  • Pre-hospital care
  • Emergency hospital care

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Gutierrez de Ceballos JP, Turégano Fuentes F, Perez Diaz D, Sanz Sanchez M, Martin Llorente C, Guerrero Sanz JE. Casualties treated at the closest hospital in the Madrid, March 11, terrorist bombings. Crit Care Med. 2005;33(1 Suppl):S107–12.

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Carresi AL. The 2004 Madrid train bombings: an analysis of pre-hospital management. Disasters. 2008;32(1):41–65.

de Ceballos JPG, Turégano-Fuentes F, Perez-Diaz D, Sanz-Sanchez M, Martin-Llorente C, Guerrero-Sanz JE. 11 March 2004: The terrorist bomb explosions in Madrid, Spain – an analysis of the logistics, injuries sustained and clinical management of casualties treated at the closest hospital. Crit Care. 2005;9(1):104–11.

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Hunt R, Ashkenazi I, Falk H. A tale of cities. Disaster Med Public Health Prep. 2011;5 Suppl 2:S185–8.

Ashkenazi I, McNulty E, Marcus L, Dorn B. The role of bystanders in mass casualty events: lessons from the 2010 Haiti earthquake. J Def Stud Resour Manag [Internet]. 2012 [cited 2018 Aug 22];01(02). Available from: .

Bazerman M, Watkins M. Predictable surprises: the disasters you should have seen coming, vol. 317. Cambridge, MA: Harvard University Press; 2004. p. 54.

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Isaac Ashkenazi

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RADM (Ret), US Public Health Service, Duluth, GA, USA

Scott D. Deitchman

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Environmental Health, Emory University, Rollins School of Public Health, Atlanta, GA, USA

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Ashkenazi, I., Deitchman, S.D., Falk, H. (2020). Case Study: The Madrid Train Bombing of March 11, 2004. In: Callaway, D., Burstein, J. (eds) Operational and Medical Management of Explosive and Blast Incidents. Springer, Cham.

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Highlighting the Potentials of Transit Oriented Development: A Case Study of Orange Line Metro Train, Lahore

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Transit Oriented Development (TOD) bids several benefits to transit users, make streets safer, and reduce dependence on automobiles, helps to reduce pollution, indorse healthy cities and enhance fare revenue to transit systems with allowing them to provide better services. The same is expected from the preferment of Transit Oriented Development along on-going Orange Line Metro Train Project in Lahore under China Pakistan Economic Corridor (CPEC). This research highlights the aptitudes for Transit Oriented Development along the ongoing project of Orange Line Metro Train Corridor, and the policy, rules and regulations that should be adopted to encourage and implement Transit Oriented Development along Orange Line route. A great potential of TOD has been identified along various sections of the Orange Line Metro Train Corridor, which can possibly be tapped by adopting such new policy guidelines for Land use zoning that can arouse TOD along the Orange Line Metro Train Lahore.

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The Yellow Train school by Chitra Vishwanath

case study on train

This school, located in Coimbatore’s hot and humid climate, caters to every small need of its users. The brief was to build a school that follows the Waldorf principles of education while adhering to the guidelines laid down by the Tamil Nadu Board of Education. Waldorf education aims to inspire life-long learning in all students and to enable them to fully develop their unique capacities. The principles of Waldorf education evolve from an understanding of human development that addresses the needs of the growing child. Children are provided with the environment necessary for the growth of their personalities as good human beings, and not just intellect. The curriculum is designed to be child-centric and focuses on mental, spiritual, physical and psychological growth along with academics.

The Yellow Train school by Chitra Vishwanath - Sheet1

The entire structure exudes a vibe of simplicity and openness. This is achieved through the use of materials, scale and simple yet effective design decisions. The earthy colour palette established by the CSEB blocks gives a subtle message about the importance of children staying connected to the earth. This colour palette itself also helps the colourful features in the interior to stand out and attract children. The tones are soothing, and the structure speaks a lot about the values that the school inculcates in its students.

The spaces are airy, naturally lit and provide ample scope for self-exploration to the kids. While being so, these spaces are also designed in a manner that the teachers find it convenient to supervise the kids’ activities. This is one of the lesser examples where the teachers’ comfort has been paid special attention to, as they are equally important users of the structure as the children. A garden with water features and pergolas, which acts as a break-out space for teachers, helps achieve this goal.

Currently, in the first phase, spaces for children from Kindergarten to 5th grade are designed to feel as cosy and comfortable as home, with toilets in close vicinity and a large activity space adjacent to it. An open-air theatre is nestled in the premises as an internal courtyard . 

The Tamil Nadu Board of Education guidelines require the floor height of the classrooms to be at least 3 metres, which felt too big for the children. Thus, structural arches were introduced to reduce the scale and bring in interesting curves into the design.

A fun campus

The Yellow Train school by Chitra Vishwanath - Sheet2

Circulation between floors is designed with ramps and makes the journey memorable. The exterior wall of the ramp has interesting jaali patterns that bring in sunlight and ventilation in a playful manner. One of the central sky-lit spaces has a net which allows the kids a free-fall feeling. A slide connects the first and second floors for the children to come down in an alternate, more fun way. A myriad of colourful tiles and floor-embedded games bring the activity area to life as children can be seen frolicking around the space, playing freely. Arches frame the classrooms, which are designed in a proportion that children can run along its entire breadth, whereas adults can only move through the centre.

The Yellow Train school by Chitra Vishwanath - Sheet3

Individual classrooms consist of different kinds of gathering spaces inside them: lecture spaces, discussion spaces and individual learning spaces. This enables various kinds of learning possible inside the same class; the teacher addresses the students, students learn from each other, and students learn by themselves from their work pinned up in the cave areas. The school also has spaces accessible only to the children. These spaces are important for the kids as exploration imbibes confidence in these developing personalities. This, along with the beautiful patterns created by the jaalis, creates a feeling of fantasy and wonder.

The Yellow Train school by Chitra Vishwanath - Sheet5

The open-air theatre brings the outdoors inside. Despite it being an introverted structure due to the climate, the connection with the sky is not lost. The children can enjoy a nice play session without the risk of over-exposure to the sun, thanks to this snug open space placed strategically. The sills and lintels of some windows extend to create boxes on which the children can sit. 

case study on train

On the first floor, along with the computer lab, lies a safely designed terrace envisaged for growing vegetables and flowers. Children on the first floor can access the playground through the dining area.

Structure and Environment

The primary building material in the walls is CSEB, which is an appropriate choice for the Coimbatore climate. Arches make an essential design element, which is also structural. For this purpose, compressed earth block precast arch panels have been used. The CSEB walls have in-built jaali patterns and steel windows which make the internal spaces energetic and playful . The jaalis help in bringing in air cooler than the outdoors, which is a much-required feature in the hot climate.

The earth required for construction was obtained from the site itself, as the play spaces and classrooms are 1.5m below road level. The structure is well lit using daylight and passively ventilated. The roof collects rainwater which is then harvested and recharged. 


  • Waldorf Education: An Introduction
  • .

The Yellow Train school by Chitra Vishwanath - Sheet1

Mrudula is an architect who believes that architecture is the ultimate art form. Her sketchbook is her constant companion on all her travels. An aficionado of all art, she can find her muse in the most mundane things.

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Interagency Coordination: A Case Study of the 2005 London Train Bombings

National Institute of Justice Journal

Archival Notice

This is an archive page that is no longer being updated. It may contain outdated information and links may no longer function as originally intended.

Subway train

On July 7, 2005, at approximately 8:50 a.m., a series of bombs exploded on three London Underground trains. One hour later, a fourth bomb exploded on the upper deck of a bus in Tavistock Square. The attacks — the work of four suicide bombers — marked the deadliest bombings in London since World War II and the first suicide attacks in modern Western Europe.

The response of London's emergency services and transportation system to the bombings is considered the city's most comprehensive and complex response ever to a terrorist attack. [1] Responding agencies faced challenges during and immediately after the attacks, but major problems in emergency coordination were minimized because London officials had established relationships with one another and had practiced agreed-upon procedures. Consequently, everyone knew their roles and responsibilities; a command and control system was up and running quickly; and mutual aid agreements — planned out in advance — were successfully initiated and applied.

This article is based on our research regarding the multiagency response to the London attacks, including barriers and ways to overcome them. As part of that National Institute of Justice-funded study, we interviewed officials from law enforcement, fire and medical services, and public health agencies who were directly involved in the July 2005 London response. [2] We asked about their role during the response, the strategies for coordination that facilitated it, the barriers they encountered and possible strategies for improving coordination among agencies responding to emergencies.

Why Do Emergency Coordination Efforts Fail?

Like the U.K., the United States faces a range of potential threats that would require a quick and coordinated response by many agencies. Our nation's capacity to prepare for and respond to terrorist attacks, natural disasters and other large-scale emergencies — especially ones involving simultaneous attacks at different locations — hinges on the ability of agencies to communicate with one another, share resources, and coordinate and execute a joint effort.

Researchers who study coordinated emergency response have identified both barriers and promising practices to help law enforcement and public health agencies improve interagency support during such situations. First and foremost, we know that multiagency coordination is a challenge at all levels. Even small problems can be exacerbated when crises occur in several places simultaneously or when reports by the media heighten public panic. Overlapping jurisdictions and responsibilities in emergency response can compound budget concerns, interagency friction and miscommunication.

In our own research, we found four general barriers to interagency coordination:

  • Communication. Agencies tend to develop their own jargon based on their areas of focus and internal workings. The subsequent lack of a common language often impedes cross-agency communication.
  • Leadership. Coordinated planning and response require an ongoing commitment from agency leaders. Response can fail when a leader of a critical partner agency is unwilling to commit qualified staff and resources because he or she is unconvinced of the benefits to the agency.
  • Cultural differences. Although public safety and health officials share the common goal of saving lives, each agency develops its own cultural standards of behavior that reflect the educational and social backgrounds of its staff, organizational hierarchy, leadership style and core mission.
  • Legal and structural differences. Each agency has a unique internal hierarchy, different processes for working through the chain of command, legal limitations, and varying geographical and topical jurisdictions. These differences can discourage, delay or prohibit joint planning initiatives.

To identify promising practices that can be used to resolve coordination barriers in the United States and elsewhere, we examined London's response in relation to a general coordination model. Applying this model — just one coordination model among many — to the 2005 bombings response provides an interesting look at some of the following interagency coordination promising practices.

See "Lessons Learned in Overcoming Barriers to Interagency Coordination."

The London Bombings: Declaring a 'Major' Incident

London's public safety agencies have been collaborating for a long time. In 1973, city leaders formed the London Emergency Services Liaison Panel (LESLP), with representatives from the London Metropolitan Police Service, City of London Police, British Transport Police, London Fire Brigade, London Ambulance Service and local London authorities. LESLP developed a manual, Major Incident Procedure Manual, [3] which is the core memorandum among the members and includes a comprehensive outline upon which London's coordination model of emergency response is founded.

The manual defines "major incident" broadly so that any emergency response agency can declare a major incident and thus increase the likelihood that multiple agencies will respond immediately. A key facet of the London bombing response was, in fact, rapid recognition and declaration of a major incident.

London's Standardized Command Structure

LESLP's manual also describes the responsibilities of each agency during any major incident and defines the general roles that relevant personnel perform on the scene. The roles are defined by three levels of leadership: Gold, Silver and Bronze. [4] The three levels of command are used across the U.K. for all large-scale emergencies. Consequently, relevant agencies are familiar with the roles and responsibilities of each level.

In addition, all agencies have agreed that the U.K.'s law enforcement serves as the coordination lead. Thus, there is no confusion about which agency is in charge during a major incident. Because these procedures were already in place at the time of the 2005 bombings, there was limited confusion about the roles and responsibilities of responding agencies.

Joint Training and Planning

The anti-terrorism branch of the London Metropolitan Police Service hosts quarterly joint exercises, known as the Hanover Series, to practice what to do in the event of a major incident. Partner agencies and other stakeholders meet in the outskirts of London for weekend tabletop exercises that increase everyone's knowledge of roles and responsibilities. According to emergency service personnel, the practice sessions also increase familiarity with other key personnel, provide the opportunity to test procedures and rehearse the standardized LESLP command and control system, and help agencies learn how to respond and react collectively.

The exercises use the Silver and Gold components of LESLP's command and control structure and therefore help reinforce and improve multiagency coordination. Perhaps most importantly, the scenarios introduced during the Hanover Series are grounded in practical, wide-ranging incidents that require in-depth planning and response duties. These exercises usually reflect local, national and international events and address a series of issues to improve multiagency cooperation.

One Voice, One Message

Having a single media spokesperson can help ensure that consistent information is released to the public in a timely manner. It can also help avoid conflicting and confusing statements from different agencies. Shortly after the 2005 bombings, the Metropolitan Police Service assumed the lead position of a joint media "cell" and convened a group of public information officials from partnering agencies and the central government. The group met quickly after the bombings to agree upon roles and responsibilities and to develop a joint message. It provided the public — via the media — with a constant stream of information that helped to restore calm and ultimately to identify the bombers.

Developing a National Coordination Model

Since 2001, there has been an increased emphasis on multiagency planning and response, and efforts have been taken in the United States and elsewhere to develop coordinated approaches. In public safety and homeland security, informal agreements between agencies can serve as a first step toward minimizing barriers to coordination. Informal agreements can allow agency leaders to achieve their goals through cooperation rather than direct competition and can help clarify each agency's expectations. After working relationships have been established, agencies may then decide to develop more formal agreements that describe the planning, collaboration and training elements discussed above.

The July 2005 bombings in London are just one example of a complex event that required extensive response planning and training. Other examples include public health outbreaks, serial violence like the D.C.-area sniper attacks and natural disasters like Hurricane Katrina. Identifying and developing a national coordination model — and learning from earlier cases — should greatly improve our nation's abilities to respond to terrorist attack or other major homeland security events.

Sidebar: Lessons Learned in Overcoming Barriers to Interagency Coordination

Our research has helped us identify several promising practices for overcoming barriers and successfully coordinating with other agencies during an emergency. These include up-front planning and ongoing collaboration and training, such as:

  • Creating and instituting standing procedures for rapidly recognizing and declaring a major multiagency incident.
  • Having a standardized process for multiagency preparation and response that is rehearsed and used regularly for major events — and, therefore, becomes familiar to all emergency response agencies.
  • Using a "liaison" model, in which personnel from one agency are assigned to work at other agencies for periods of time; sharing staff in this way facilitates communication and on-site consultation across agencies.
  • Developing relationships to facilitate cooperation among agencies by holding joint trainings, planning sessions and informal social events (such as off-site dinners).
  • Encouraging participation of all relevant agencies' senior and junior staff in joint training and planning sessions to foster relationship building, communication, trust and appreciation for each other's roles.
  • Providing continued reinforcement from senior management through ongoing support for annual trainings and interactions and dedicating resources to joint initiatives.
  • Implementing procedures to coordinate and send joint messages to the news media to forestall panic and exaggerated public perceptions.

Editor's Note: In the next issue of the NIJ Journal , we will further discuss challenges faced by the British agencies in responding to the 2005 London bombings and lessons learned from them.

Return to text .

About This Article

This article appeared in NIJ Journal Issue 260 , July 2008.

[note 1] London Regional Resilience Forum, Looking Back, Moving Forward. The Multi-Agency Debrief: Lessons Identified and Progress Since the Terrorist Events of 7 July 2005 (pdf, 61 pages)   , London: Government Office for London, 2006.

[note 2] The authors thank the London planning and response community for their candid and thoughtful participation in this study; this project would not have been possible without their support.

[note 3] London Emergency Services Liaison Panel (LESLP), Major Incident Procedure Manual, Sixth Edition, London: Metropolitan Police Service, 2004.

[note 4] These levels of command are often called "strategic," "tactical" and "operational." In London's emergency command structure, these roles are not related to rank within or across agencies.

About the author

Kevin J. Strom is a senior scientist in RTI International's Center for Crime, Violence, and Justice Research. He has 12 years of experience in criminal justice research, including law enforcement responses to community violence, the causes of interpersonal violence and interagency coordination in response to terrorism. Joe Eyerman is a senior research methodologist and director of the Health Security Program at RTI International. He has 17 years of experience with quantitative and qualitative modeling and analysis of social behavior; his primary research interest is in the formal and statistical modeling of decision processes related to individual and organizational political behavior, violence and terrorism.

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Title: enhancing space situational awareness to mitigate risk: a single-case study in the misidentification of a recently-launched starlink satellite train as a uap in commercial aviation.

Abstract: Over the past several years, the misidentification of SpaceX Starlink satellites as Unidentified Aerial Phenomena (UAP) by pilots and laypersons has generated unnecessary aviation risk and confusion. The many deployment and orbital evolution strategies, coupled with changing sun specular reflection angles, contribute to this gap in space situational awareness. In this paper we present a case analysis of an incident that generated multiple, corroborating reports of a UAP from five pilots on two commercial airline flights over the Pacific Ocean on August 10th, 2022. This incident included two cell phone photos and a video of an unrecognizable and possibly anomalous phenomenon. We then use supplemental two-line elements (TLEs) for the Starlink train of satellites launched that same day and Automatic Dependent Surveillance Broadcast (ADS-B) data from the flight with the photographs to reconstruct a view of these satellites from the cockpit at the time and place of the sighting. The success of this work demonstrates an approach that could, in principle, warn aviators about satellites that could be visible in unusual or novel illumination configurations, thus increasing space situational awareness and supporting aviation safety. We conclude with recommendations for governments and satellite operators to provide better a-priori data that can be used to create advisories to aviators and the public. The automated simulation of known specular reflection off constellations of satellites could also support researchers investigating sightings of unfamiliar aerial or aerospace objects as likely being from normal versus novel space events.

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