reactive dynamic traffic assignment

• Architecture and Design
• Asian and Pacific Studies
• Business and Economics
• Classical and Ancient Near Eastern Studies
• Computer Sciences
• Cultural Studies
• Engineering
• General Interest
• Geosciences
• Industrial Chemistry
• Islamic and Middle Eastern Studies
• Jewish Studies
• Library and Information Science, Book Studies
• Life Sciences
• Linguistics and Semiotics
• Literary Studies
• Materials Sciences
• Mathematics
• Social Sciences
• Sports and Recreation
• Theology and Religion
• Publish your article
• The role of authors
• Promoting your article
• Abstracting & indexing
• Publishing Ethics
• Why publish with De Gruyter
• How to publish with De Gruyter
• Our book series
• Our subject areas
• Your digital product at De Gruyter
• Contribute to our reference works
• Product information
• Tools & resources
• Product Information
• Promotional Materials
• Orders and Inquiries
• FAQ for Library Suppliers and Book Sellers
• Repository Policy
• Free access policy
• Open Access agreements
• Database portals
• For Authors
• Customer service
• People + Culture
• Journal Management
• How to join us
• Working at De Gruyter
• Mission & Vision
• De Gruyter Foundation
• De Gruyter Ebound
• Our Responsibility
• Partner publishers

Your purchase has been completed. Your documents are now available to view.

Dynamic traffic assignment: model classifications and recent advances in travel choice principles

Dynamic Traffic Assignment (DTA) has been studied for more than four decades and numerous reviews of this research area have been conducted. This review focuses on the travel choice principle and the classification of DTA models, and is supplementary to the existing reviews. The implications of the travel choice principle for the existence and uniqueness of DTA solutions are discussed, and the interrelation between the travel choice principle and the traffic flow component is explained using the nonlinear complementarity problem, the variational inequality problem, the mathematical programming problem, and the fixed point problem formulations. This paper also points out that all of the reviewed travel choice principles are extended from those used in static traffic assignment. There are also many classifications of DTA models, in which each classification addresses one aspect of DTA modeling. Finally, some future research directions are identified.

[1] Gomes G., Horowitz R., Optimal freeway ramp metering using the asymmetric cell transmission model, Transport. Res. C-Emer., 2006, 14, 244–262 http://dx.doi.org/10.1016/j.trc.2006.08.001 10.1016/j.trc.2006.08.001 Search in Google Scholar

[2] Meng Q., Khoo H.L., A Pareto-optimization approach for a fair ramp metering, Transport. Res. C-Emer., 2010, 18, 489–506 http://dx.doi.org/10.1016/j.trc.2009.10.001 10.1016/j.trc.2009.10.001 Search in Google Scholar

[3] Park B.B., Yun I., Ahn K., Stochastic optimization for sustainable traffic signal control, Int. J. Sustain. Transp., 2009, 3, 263–284 http://dx.doi.org/10.1080/15568310802091053 10.1080/15568310802091053 Search in Google Scholar

[4] Park B.B., Kamarajugadda A., Development and evaluation of a stochastic traffic signal optimization method, Int. J. Sustain. Transp., 2007, 1, 193–207 http://dx.doi.org/10.1080/15568310600737568 10.1080/15568310600737568 Search in Google Scholar

[5] Lo H.K., Szeto W.Y., Modeling advanced traveler information services: static versus dynamic paradigms, Transport. Res. B-Meth., 2004, 38, 495–515 http://dx.doi.org/10.1016/j.trb.2003.06.001 10.1016/j.trb.2003.06.001 Search in Google Scholar

[6] Szeto W.Y., Lo H.K., The impact of advanced traveler information services on travel time and schedule delay costs, J. Intell. Transport. S., 2005, 9, 47–55 http://dx.doi.org/10.1080/15472450590916840 10.1080/15472450590916840 Search in Google Scholar

[7] Lo H.K., Szeto W.Y., Road pricing modeling for hypercongestion, Transport. Res. A-Pol., 2005, 39, 705–722 10.1016/j.tra.2005.02.019 Search in Google Scholar

[8] Zhong R.X., Sumalee A., Maruyama T., Dynamic marginal cost, access control, and pollution charge: a comparison of bottleneck and whole link models, J. Adv. Transport., 2011, Accepted 10.1002/atr.195 Search in Google Scholar

[9] Ukkusuri S., Waller S., Linear programming models for the user and system optimal dynamic network design problem: formulations, comparisons and extensions, Netw. Spat. Econ., 2008, 8, 383–406 http://dx.doi.org/10.1007/s11067-007-9019-6 10.1007/s11067-007-9019-6 Search in Google Scholar

[10] Szeto W.Y., Ghosh, B., Basu, B., O’Mahony M., Cell-based short-term traffic flow forecasting using time series modelling, Transport. Eng.-J. Asce., 2009, 135, 658–667 http://dx.doi.org/10.1061/(ASCE)0733-947X(2009)135:9(658) 10.1061/(ASCE)0733-947X(2009)135:9(658) Search in Google Scholar

[11] Abdelghany A.F., Adbelghany K.F., Mahmassani H.S., Murray P.M., Dynamic traffic assignment in design and evaluation of high-occupancy toll lanes, Transport. Res. Rec., 2000, 1733, 39–48 http://dx.doi.org/10.3141/1733-06 10.3141/1733-06 Search in Google Scholar

[12] Yagar S., Dynamic traffic assignment by individual path minimization and queuing, Transport. Res., 1971, 5, 179–196 http://dx.doi.org/10.1016/0041-1647(71)90020-7 10.1016/0041-1647(71)90020-7 Search in Google Scholar

[13] Cascetta E., Cantarella G.E., Modelling dynamics in transportation networks: state of the art and future developments, Simulat. Pract. Theory., 1993, 15, 65–91 http://dx.doi.org/10.1016/0928-4869(93)90017-K 10.1016/0928-4869(93)90017-K Search in Google Scholar

[14] Peeta S., Ziliaskopoulos A.K., Foundations of dynamic traffic assignment: the past, the present and the future, Netw. Spat. Econ., 2001, 1, 233–265 http://dx.doi.org/10.1023/A:1012827724856 10.1023/A:1012827724856 Search in Google Scholar

[15] Boyce D., Lee D.H., Ran B., Analytical models of the dynamic traffic assignment problem, Netw. Spat. Econ., 2001, 1, 377–390 http://dx.doi.org/10.1023/A:1012852413469 10.1023/A:1012852413469 Search in Google Scholar

[16] Szeto W.Y., Lo H.K., Dynamic traffic assignment: review and future, Information Technology, 2005, 5, 85–100 Search in Google Scholar

[17] Szeto W.Y., Lo H.K., Properties of dynamic traffic assignment with physical queues, J. East Asia Soc. Transport. Stud., 2005, 6, 2108–2123 Search in Google Scholar

[18] Mun J.S., Traffic performance models for dynamic traffic assignment: an assessment of existing models, Transport. Rev., 2007, 27, 231–249 http://dx.doi.org/10.1080/01441640600979403 10.1080/01441640600979403 Search in Google Scholar

[19] Jeihani M., A review of dynamic traffic assignment computer packages, J. Transport. Res. Forum, 2007, 46, 35–46 Search in Google Scholar

[20] Szeto W.Y., Cell-based dynamic equilibrium models, dynamic traffic assignment and signal control, in: Dynamic route guidance and traffic control, complex social, Economic and Engineered Networks Series, Ukkusuri S., Ozbay K. (Eds.), Springer, 2011, submitted Search in Google Scholar

[21] Wardrop J., Some theoretical aspects of road traffic research, ICE Proceedings: Part II, Engineering Divisions, 1952, 1, 325–362 http://dx.doi.org/10.1680/ipeds.1952.11259 10.1680/ipeds.1952.11259 Search in Google Scholar

[22] Oppenheim N., Urban travel demand modelling: from individual choice to general equilibrium, John Wiley & Sons, USA, 1995 Search in Google Scholar

[23] Daganzo C.F., Sheffi Y., On stochastic models of traffic assignment, Transport. Sci., 1977, 11, 253–274 http://dx.doi.org/10.1287/trsc.11.3.253 10.1287/trsc.11.3.253 Search in Google Scholar

[24] Hall R.W., Travel outcome and performance: the effect of uncertainty on accessibility, Transport. Res. BMeth., 1993, 17, 275–290 http://dx.doi.org/10.1016/0191-2615(83)90046-2 10.1016/0191-2615(83)90046-2 Search in Google Scholar

[25] Lo H.K., Luo X.W., Siu B.W.Y., Degradable transport network: travel time budget of travellers with heterogeneous risk aversion, Transport. Res. B-Meth., 2006, 40, 792–806 http://dx.doi.org/10.1016/j.trb.2005.10.003 10.1016/j.trb.2005.10.003 Search in Google Scholar

[26] Uchida T., Iida Y., Risk assignment: a new traffic assignment model considering the risk travel time variation, In: Daganzo C.F. (Ed.), Proceedings of the 12th International Symposium on Transportation and Traffic Theory (July 21–23, 1993, Berkeley, CA, USA), Elsevier, Amsterdam, 1993, 89–105 Search in Google Scholar

[27] Jackson W.B., Jucker J.V., An empirical study of travel time variability and travel choice behavior, Transport. Sci., 1982, 16, 460–475 http://dx.doi.org/10.1287/trsc.16.4.460 10.1287/trsc.16.4.460 Search in Google Scholar

[28] Brastow W.C., Jucker J.V., Use of a mean variance criterion for traffic assignment, unpublished manuscript, Department of Industrial Engineering, Stanford University, 1977 Search in Google Scholar

[29] Szeto W.Y., Solayappan M., Jiang Y., Reliabilitybased transit assignment for congested stochastic transit networks, Comput.-Aided Civ. Inf., 2011, 26, 311–326 http://dx.doi.org/10.1111/j.1467-8667.2010.00680.x 10.1111/j.1467-8667.2010.00680.x Search in Google Scholar

[30] Chen A., Zhou Z., The α-reliable mean-excess traffic equilibrium model with stochastic travel times, Transport. Res. B-Meth., 2010, 44, 493–513 http://dx.doi.org/10.1016/j.trb.2009.11.003 10.1016/j.trb.2009.11.003 Search in Google Scholar

[31] Franklin J.P., Karlstrom A., Travel time reliability for Stockholm roadways: modeling the mean lateness factor, Transport. Res. Rec., 2009, 2134, 106–113 http://dx.doi.org/10.3141/2134-13 10.3141/2134-13 Search in Google Scholar

[32] Chan K.S., Lam W.H.K., Impact of road pricing on the road network reliability, J. East Asia Soc. Transport. Stud., 2005, 6, 2060–2075 Search in Google Scholar

[33] Ordóñez F., Stier-Moses N.E., Wardrop equilibria with risk-averse users, Transport. Sci., 2010, 44, 63–86 http://dx.doi.org/10.1287/trsc.1090.0292 10.1287/trsc.1090.0292 Search in Google Scholar

[34] Lam T., Small K., The value of time and reliability: measurement from a value pricing experiment, Transport. Res. E-Log., 2001, 37, 231–251 http://dx.doi.org/10.1016/S1366-5545(00)00016-8 10.1016/S1366-5545(00)00016-8 Search in Google Scholar

[35] Bell M.G.H., Cassir C., Risk-averse user equilibrium traffic assignment: an application of game theory, Transport. Res. B-Meth., 2002, 36, 671–681 http://dx.doi.org/10.1016/S0191-2615(01)00022-4 10.1016/S0191-2615(01)00022-4 Search in Google Scholar

[36] Szeto W.Y., O’Brien L., O’Mahony M., Generalisation of the risk-averse traffic assignment, In: Bell M.G.H., Heydecker B.G., Allsop R.E. (Eds.), Proceedings of 17th International Symposium on Transportation and Traffic Theory (July 23–25, 2007, London, UK), Emerald Group Publishing Limited, 2007, 127–153 Search in Google Scholar

[37] Szeto W.Y., O’Brien L., O’Mahony M., Measuring network reliability by considering paradoxes: the multiple network demon approach, Transport. Res. Rec., 2009, 2090, 42–50 http://dx.doi.org/10.3141/2090-05 10.3141/2090-05 Search in Google Scholar

[38] Szeto W.Y., Cooperative game approaches to measuring network reliability considering paradoxes, Transport. Res. C-Emer., 2011, 11, 229–241 http://dx.doi.org/10.1016/j.trc.2010.05.010 10.1016/j.trc.2010.05.010 Search in Google Scholar

[39] Szeto W.Y., O’Brien L., O’Mahony M., Risk-averse traffic assignment with elastic demand: NCP formulation and solution method for assessing performance reliability, Netw. Spat. Econ., 2006, 6, 313–332 http://dx.doi.org/10.1007/s11067-006-9286-7 10.1007/s11067-006-9286-7 Search in Google Scholar

[40] Ordóñez F., Stier-Moses N.E., Robust Wardrop equilibrium, Netw. Control. Optim., 2007, 4465, 247–256 http://dx.doi.org/10.1007/978-3-540-72709-5_26 10.1007/978-3-540-72709-5_26 Search in Google Scholar

[41] Zhang C., Chen X., Sumalee A., Robust Wardrop’s user equilibrium assignment under stochastic demand and supply: expected residual minimization approach, Transport. Res. B-Meth., 2011, 45, 534–552 http://dx.doi.org/10.1016/j.trb.2010.09.008 10.1016/j.trb.2010.09.008 Search in Google Scholar

[42] Tversky A., Kahneman D., Advances in prospect theory: cumulative representation of uncertainty, J. Risk Uncertainty, 1992, 5, 297–323 http://dx.doi.org/10.1007/BF00122574 10.1007/BF00122574 Search in Google Scholar

[43] Kahneman D., Tversky A., Prospect theory: an analysis of decisions under risk, Econometrica, 1979, 47, 263–291 http://dx.doi.org/10.2307/1914185 10.2307/1914185 Search in Google Scholar

[44] Thaler R.H., Tversky A., Kahneman D., Schwartz A., The effect of myopia and loss aversion on risk taking: an experimental test, Quart. J. Econ., 1997, 112, 647–661 http://dx.doi.org/10.1162/003355397555226 10.1162/003355397555226 Search in Google Scholar

[45] Camerer C.F., Ho T.H., Violations of the betweenness axiom and nonlinearity in probability, J. Risk Uncertainty., 1994, 8, 167–196 http://dx.doi.org/10.1007/BF01065371 10.1007/BF01065371 Search in Google Scholar

[46] Avineri E., Prashker J.N., Violations of expected utility theory in route-choice stated preferences: certainty effect and inflating of small probabilities, Transport. Res. Rec., 2004, 1894, 222–229 http://dx.doi.org/10.3141/1894-23 10.3141/1894-23 Search in Google Scholar

[47] Avineri E., The effect of reference point on stochastic network equilibrium, Transport. Sci., 2006, 40, 409–420 http://dx.doi.org/10.1287/trsc.1060.0158 10.1287/trsc.1060.0158 Search in Google Scholar

[48] Mirchandani P., Soroush H., Generalized traffic equilibrium with probabilistic travel times and perceptions, Transport. Sci., 1987, 21, 133–152 http://dx.doi.org/10.1287/trsc.21.3.133 10.1287/trsc.21.3.133 Search in Google Scholar

[49] Shao H., Lam W.H.K., Tam M.L., A reliability-based stochastic traffic assignment model for network with multiple user classes under uncertainty in demand, Netw. Spat. Econ., 2006, 6, 173–204 http://dx.doi.org/10.1007/s11067-006-9279-6 10.1007/s11067-006-9279-6 Search in Google Scholar

[50] Chen A., Zhou Z., A stochastic α-reliable mean-excess traffic equilibrium model with probabilistic travel times and perception errors, In: Lam W.H.K., Wong S.C., Lo H.K. (Eds.), Proceedings of 18th International Symposium on Transportation and Traffic Theory 2009: Golden Jubilee (July 16–18, 2009), Springer, 2009, 117–145 Search in Google Scholar

[51] Chu Y.L., Work departure time analysis using dogit ordered generalized extreme value model, Transport. Res. Rec., 2009, 2132, 42–49 http://dx.doi.org/10.3141/2132-05 10.3141/2132-05 Search in Google Scholar

[52] Jou R.C., Kitamura R., Weng M.C., Chen C.C., Dynamic commuter departure time choice under uncertainty, Transport. Res. A-Pol., 2008, 42, 774–783 10.1016/j.tra.2008.01.017 Search in Google Scholar

[53] Lemp J.D., Kockelman K.M., Empirical investigation of continuous logit for departure time choice with Bayesian methods, Transport. Res. Rec., 2010, 2165, 59–68 http://dx.doi.org/10.3141/2165-07 10.3141/2165-07 Search in Google Scholar

[54] Lemp J.D., Kockelman K.M., Damien P., The continuous cross-nested logit model: Formulation and application for departure time choice, Transport. Res. B-Meth., 2010, 44, 646–661 http://dx.doi.org/10.1016/j.trb.2010.03.001 10.1016/j.trb.2010.03.001 Search in Google Scholar

[55] Friesz T.L., Luque F.J., Tobin R.L., Wie B.W., Dynamic network traffic assignment considered as a continuous time optimal control problem, Oper. Res., 1989, 37, 893–901 http://dx.doi.org/10.1287/opre.37.6.893 10.1287/opre.37.6.893 Search in Google Scholar

[56] Vickrey W.S., Congestion theory and transport investment, Am. Econ. Rev., 1969, 59, 251–261 Search in Google Scholar

[57] Mahmassani H.S., Herman R., Dynamic user equilibrium departure time and route choice on an idealized traffic arterials, Transport. Sci., 1984, 18, 362–384 http://dx.doi.org/10.1287/trsc.18.4.362 10.1287/trsc.18.4.362 Search in Google Scholar

[58] Merchant D.K., Nemhauser G.L., A model and an algorithm for the dynamic traffic assignment problem, Transport. Sci., 1978, 12, 183–199 http://dx.doi.org/10.1287/trsc.12.3.183 10.1287/trsc.12.3.183 Search in Google Scholar

[59] Small, K.A., The scheduling of consumer activities: work trips, Am. Econ. Rev., 1982, 72, 467–479 Search in Google Scholar

[60] Ran B., Boyce D.E., Modelling dynamic transportation networks: an intelligent transportation system oriented approach, 2nd rev. ed., Springer, Berlin, 1996 10.1007/978-3-642-80230-0_14 Search in Google Scholar

[61] Vythoulkas P.C., Two models for predicting dynamic stochastic equilibria in urban transportation networks, In: Koshi M. (Ed.), Proceedings of 11th International Symposium on Transportation and Traffic Theory (July 18–20, Yokohama, Japan), Elsevier, 1990, 253–272 Search in Google Scholar

[62] Boyce D.E., Ran B., Li I.Y., Considering travellers’ risk-taking behavior in dynamic traffic assignment, in: Bell M.G.H., Transportation networks: recent methodological advances, Elsevier, Oxford, 1999 Search in Google Scholar

[63] Szeto W.Y., Jiang Y, Sumalee A., A cell-based model for multi-class doubly stochastic dynamic traffic assignment, Comput-Aided Civ. Inf., 2011, 26, 595–611 http://dx.doi.org/10.1111/j.1467-8667.2011.00717.x 10.1111/j.1467-8667.2011.00717.x Search in Google Scholar

[64] Simon H.A., Models of bounded rationality (Vol. 3), The MIT Press, Cambridge, 1997 10.7551/mitpress/4711.001.0001 Search in Google Scholar

[65] Simon H.A., A behavioral model of rational choice, Quart. J. Econ., 1955, 69, 99–118 http://dx.doi.org/10.2307/1884852 10.2307/1884852 Search in Google Scholar

[66] Simon H.A., Models of Man, Wiley, New York, 1957 10.2307/2550441 Search in Google Scholar

[67] Mahmassani H.S., Chang G.L., On boundedly-rational user equilibrium in transportation systems, Transport. Sci., 1987, 21, 89–99 http://dx.doi.org/10.1287/trsc.21.2.89 10.1287/trsc.21.2.89 Search in Google Scholar

[68] Szeto W.Y., Lo H.K., Dynamic traffic assignment: properties and extensions, Transportmetrica, 2006, 2, 31–52 http://dx.doi.org/10.1080/18128600608685654 10.1080/18128600608685654 Search in Google Scholar

[69] Lou Y., Yin Y., Lawphongpanich S., Robust congestion pricing under boundedly rational user equilibrium, Transport. Res. B-Meth., 2010, 44, 15–28 http://dx.doi.org/10.1016/j.trb.2009.06.004 10.1016/j.trb.2009.06.004 Search in Google Scholar

[70] zeto W.Y., Lo H.K., A cell-based simultaneous route and departure time choice model with elastic demand, Transport. Res. B-Meth., 2004, 38, 593–612 http://dx.doi.org/10.1016/j.trb.2003.05.001 10.1016/j.trb.2003.05.001 Search in Google Scholar

[71] Pel A.J., Bliemer M.C.J., Hoogendoorn S.P., Hybrid route choice modeling in dynamic traffic assignment, Transport. Res. Rec., 2009, 2091, 100–107 http://dx.doi.org/10.3141/2091-11 10.3141/2091-11 Search in Google Scholar

[72] Kuwahara M., Akamatsu T., Decomposition of the reactive assignments with queues for many-to-many origin-destination pattern, Transport. Res. B-Meth., 1997, 31, 1–10 http://dx.doi.org/10.1016/S0191-2615(96)00020-3 10.1016/S0191-2615(96)00020-3 Search in Google Scholar

[73] Ben-akiva M., De Palma A., Kaysi, I., Dynamic network models and driver information system, Transport. Res. A-Pol., 1991, 25, 251–266 http://dx.doi.org/10.1016/0191-2607(91)90142-D 10.1016/0191-2607(91)90142-D Search in Google Scholar

[74] Sumalee A., Zhong, R.X., Pan, T.L., Szeto, W.Y., Stochastic cell transmission model (SCTM): a stochastic dynamic traffic model for traffic state surveillance and assignment, Transport. Res. B-Meth., 2011, 45, 507–533 http://dx.doi.org/10.1016/j.trb.2010.09.006 10.1016/j.trb.2010.09.006 Search in Google Scholar

[75] Lo H.K., Szeto W.Y., A cell-based dynamic traffic assignment model: formulation and properties, Math. Comput. Model., 2002, 35, 849–865 http://dx.doi.org/10.1016/S0895-7177(02)00055-9 10.1016/S0895-7177(02)00055-9 Search in Google Scholar

[76] Tong C.O., Wong S.C., A predictive dynamic traffic assignment model in congested capacity-constrained road networks, Transport. Res. B-Meth., 2000, 34, 625–644 http://dx.doi.org/10.1016/S0191-2615(99)00045-4 10.1016/S0191-2615(99)00045-4 Search in Google Scholar

[77] Horowitz J.L., The stability of stochastic equilibrium in a two-link transportation network, Transport. Res. B-Meth., 1984, 18, 13–28 http://dx.doi.org/10.1016/0191-2615(84)90003-1 10.1016/0191-2615(84)90003-1 Search in Google Scholar

[78] Cascetta E., A stochastic process approach to the analysis of temporal dynamics in transportation networks, Transport. Res. B-Meth., 1989, 23, 1–17 http://dx.doi.org/10.1016/0191-2615(89)90019-2 10.1016/0191-2615(89)90019-2 Search in Google Scholar

[79] Chang G.L., Mahmassani H., Travel time prediction and departure time adjustment behavior dynamics in a congested traffic system, Transport. Res. B-Meth., 1988, 22, 217–232 http://dx.doi.org/10.1016/0191-2615(88)90017-3 10.1016/0191-2615(88)90017-3 Search in Google Scholar

[80] Lam W.H.K., Huang H.J., Dynamic user optimal traffic assignment model for many to one travel demand, Transport. Res. B-Meth., 1995, 29, 243–259 http://dx.doi.org/10.1016/0191-2615(95)00001-T 10.1016/0191-2615(95)00001-T Search in Google Scholar

[81] Ran B., Boyce D., A link-based variational inequality formulation of ideal dynamic user-optimal route choice problem, Transport. Res. C-Emer., 1996, 4, 1–12 http://dx.doi.org/10.1016/0968-090X(95)00017-D 10.1016/0968-090X(95)00017-D Search in Google Scholar

[82] Carey M., Optimal time-varying flows on congested network, Oper. Res., 1987, 35, 58–69 http://dx.doi.org/10.1287/opre.35.1.58 10.1287/opre.35.1.58 Search in Google Scholar

[83] Carey M., Srinivasan A., Congested network flows: time-varying demands and start-time policies, Eur. J. Oper. Res., 1988, 36, 227–240 http://dx.doi.org/10.1016/0377-2217(88)90429-8 10.1016/0377-2217(88)90429-8 Search in Google Scholar

[84] Lo H.K., Szeto W.Y., A cell-based variational inequality formulation of the dynamic user optimal assignment problem, Transport. Res. B-Meth., 2002, 36, 421–443 http://dx.doi.org/10.1016/S0191-2615(01)00011-X 10.1016/S0191-2615(01)00011-X Search in Google Scholar

[85] Szeto W.Y., The enhanced lagged cell transmission model for dynamic traffic assignment, Transport. Res. Rec., 2008, 2085, 76–85 http://dx.doi.org/10.3141/2085-09 10.3141/2085-09 Search in Google Scholar

[86] Lo H.K., Ran B., Hongola B., Multiclass dynamic traffic assignment model: formulation and computational experiences, Transport. Res. Rec., 1996, 1537, 74–82 http://dx.doi.org/10.3141/1537-11 10.1177/0361198196153700111 Search in Google Scholar

[87] Liu Y.H., Mahmassani H.S., Dynamic aspects of commuter decisions under advanced traveler information systems — modeling framework and experimental results, Transport. Res. Rec., 1998, 1645, 111–119 http://dx.doi.org/10.3141/1645-14 10.3141/1645-14 Search in Google Scholar

[88] Ben-Akiva M., Cuneo D., Hasan M., Jha M., et al., Evaluation of freeway control using a microscopic simulation laboratory, Transport. Res. C-Emer., 2003, 11, 29–50 http://dx.doi.org/10.1016/S0968-090X(02)00020-7 10.1016/S0968-090X(02)00020-7 Search in Google Scholar

[89] Jiang Y.Q., Wong S.C., Ho H.W., Zhang P., et al., A dynamic traffic assignment model for a continuum transportation system, Transport. Res. B-Meth., 2011, 45, 343–363 http://dx.doi.org/10.1016/j.trb.2010.07.003 10.1016/j.trb.2010.07.003 Search in Google Scholar

[90] Hughes R.L., A continuum theory for the flow of pedestrians, Transport. Res. B-Meth., 2002, 36, 507–535 http://dx.doi.org/10.1016/S0191-2615(01)00015-7 10.1016/S0191-2615(01)00015-7 Search in Google Scholar

[91] Hoogendoorn S.P., Bovy P.H.L., Dynamic useroptimal assignment in continuous time and space, Transport. Res. B-Meth., 2004, 38, 571–592 http://dx.doi.org/10.1016/j.trb.2002.12.001 10.1016/j.trb.2002.12.001 Search in Google Scholar

[92] Hoogendoorn S.P., Bovy P.H.L., Pedestrian routechoice and activity scheduling theory and models, Transport. Res. B-Meth., 2004, 38, 169–190 http://dx.doi.org/10.1016/S0191-2615(03)00007-9 10.1016/S0191-2615(03)00007-9 Search in Google Scholar

[93] Xia Y., Wong S.C., Zhang M., Shu C.W., et al., An efficient discontinuous Galerkin method on triangular meshes for a pedestrian flow model, Int. J. Numer. Meth. Eng., 2008, 76, 337–350 http://dx.doi.org/10.1002/nme.2329 10.1002/nme.2329 Search in Google Scholar

[94] Huang L., Wong S.C., Zhang M.P., Shu C.W., et al., Revisiting Hughes’ dynamic continuum model for pedestrian flow and the development of an efficient solution algorithm. Transport. Res. B-Meth., 2009, 43, 127–141 http://dx.doi.org/10.1016/j.trb.2008.06.003 10.1016/j.trb.2008.06.003 Search in Google Scholar

[95] Huang L., Xia Y., Wong S.C., Shu C.W., et al., Dynamic continuum model for bi-directional pedestrian flows, P. I. Civil Eng.: Eng. Comput. Mec., 2009, 162, 67–75 10.1680/eacm.2009.162.2.67 Search in Google Scholar

[96] Xia Y., Wong S.C., Shu C.W., Dynamic continuum pedestrian flow model with memory effect, Phys. Rev. E, 2009, 79, 066113 http://dx.doi.org/10.1103/PhysRevE.79.066113 10.1103/PhysRevE.79.066113 Search in Google Scholar

[97] Jiang Y.Q., Xiong T., Wong S.C., Shu C.W., et al., A reactive dynamic continuum user equilibrium model for bi-directional pedestrian flows, Acta. Math. Sci., 2009, 29, 1541–1555 10.1016/S0252-9602(10)60002-1 Search in Google Scholar

[98] Guo R.Y., Huang H.J., Wong S.C., Collection, spillback, and dissipation in pedestrian evacuation: a network-based method, Transport. Res. B-Meth., 2011, 45, 490–506 http://dx.doi.org/10.1016/j.trb.2010.09.009 10.1016/j.trb.2010.09.009 Search in Google Scholar

[99] Xiong T., Zhang M., Shu C.W., Wong S.C., et al., Highorder computational scheme for a dynamic continuum model for bi-directional pedestrian flows, Comput.—Aided Civ. Inf., 2011, 26, 298–310 http://dx.doi.org/10.1111/j.1467-8667.2010.00688.x 10.1111/j.1467-8667.2010.00688.x Search in Google Scholar

[100] Nagurney A., Network economics: a variational inequality approach, Kluwer Academic Publishers, Norwell, Massachusetts, 1993 http://dx.doi.org/10.1007/978-94-011-2178-1 10.1007/978-94-011-2178-1 Search in Google Scholar

[101] Chow A.H.F., Properties of system optimal traffic assignment with departure time choice and its solution method, Transport. Res. B-Meth., 2009, 43, 325–344 http://dx.doi.org/10.1016/j.trb.2008.07.006 10.1016/j.trb.2008.07.006 Search in Google Scholar

[102] Chow A.H.F., Dynamic system optimal traffic assignment — a state-dependent control theoretic approach, Transportmetrica, 2009, 5, 85–106 http://dx.doi.org/10.1080/18128600902717483 10.1080/18128600902717483 Search in Google Scholar

[103] Long J.C., Szeto W.Y., Gao Z.Y., Discretised link travel time models based on cumulative flows: formulations and properties, Transport. Res. B-Meth., 2011, 45, 232–254 http://dx.doi.org/10.1016/j.trb.2010.05.002 10.1016/j.trb.2010.05.002 Search in Google Scholar

© 2011 Versita Warsaw

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Supplementary Materials

Please login or register with De Gruyter to order this product.

Journal and Issue

Articles in the same issue.

• View Record

TRID the TRIS and ITRD database

A REACTIVE DYNAMIC ASSIGNMENT SCHEME

In this paper we consider two traffic control strategies relying on user response to information and/or flow restriction. Ultimately, the control strategies are designed to function in real time, hence provide command values based on actual conditions and requiring little computational effort. The proposed control strategies are based on the idea that the network load, as measured by instantaneous travel times for instance, should be shared as equally as possible between paths. In order to achieve such an aim, the commands are designed to make the system state converge towards a state in which instantaneous travel times of paths relative to any given origin-destination (OD) tend to be equal.

• Find a library where document is available. Order URL: http://worldcat.org/isbn/0080434304
• This book is also available in the USA and Canada from Elsevier Science Inc., P.O. Box 945, Madison Square Station, New York, NY 10160-0757.
• Khoshyaran, M M
• Lebacque, J P
• Third IMA International Conference on Mathematics in Transport Planning and Control
• Location: Cardiff, United Kingdom
• Date: 1998-4-1 to 1998-4-3
• Publication Date: 1998
• Features: References;
• Pagination: p. 131-143

Subject/Index Terms

• TRT Terms: Dynamic traffic assignment ; Interrupted flow ; Reaction time ; Traffic control ; Travel time ; Traveler information and communication systems
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 00763018
• Record Type: Publication
• ISBN: 0080434304
• Files: TRIS
• Created Date: Apr 12 1999 12:00AM

To read this content please select one of the options below:

Please note you do not have access to teaching notes, a reactive dynamic assignment scheme.

Mathematics in Transport Planning and Control

ISBN : 978-0-08-043430-8 , eISBN : 978-0-58-547418-2

Publication date: 15 December 1998

In this paper we consider two traffic control strategies relying on user response to information and/or flow restriction. Ultimately, the control strategies are designed to function in real time, hence provide command values based on actual conditions and requiring little computational effort. The proposed control strategies are based on the idea that the network load, as measured by instantaneous travel times for instance, should be shared as equally as possible between paths. In order to achieve such an aim, the commands are designed to make the system state converge towards a state in which instantaneous travel times of paths relative to any given OD tend to be equal.

Khoshyaran, M.M. and Lebacque, J.P. (1998), "A Reactive Dynamic Assignment Scheme", Griffiths, J.D. (Ed.) Mathematics in Transport Planning and Control , Emerald Group Publishing Limited, Leeds, pp. 131-143. https://doi.org/10.1108/9780585474182-013

Emerald Group Publishing Limited

Copyright © 1998 Emerald Group Publishing Limited

We’re listening — tell us what you think

Something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Dynamic traffic assignment: model classifications and recent advances in travel choice principles

• Review Article
• Published: 21 November 2011
• Volume 2 , pages 1–18, ( 2012 )

Cite this article

• W. Y. Szeto 1 &
• S. C. Wong 1  

215 Accesses

39 Citations

Explore all metrics

Dynamic Traffic Assignment (DTA) has been studied for more than four decades and numerous reviews of this research area have been conducted. This review focuses on the travel choice principle and the classification of DTA models, and is supplementary to the existing reviews. The implications of the travel choice principle for the existence and uniqueness of DTA solutions are discussed, and the interrelation between the travel choice principle and the traffic flow component is explained using the nonlinear complementarity problem, the variational inequality problem, the mathematical programming problem, and the fixed point problem formulations. This paper also points out that all of the reviewed travel choice principles are extended from those used in static traffic assignment. There are also many classifications of DTA models, in which each classification addresses one aspect of DTA modeling. Finally, some future research directions are identified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Gomes G., Horowitz R., Optimal freeway ramp metering using the asymmetric cell transmission model, Transport. Res. C-Emer., 2006, 14, 244–262

Article   Google Scholar  

Meng Q., Khoo H.L., A Pareto-optimization approach for a fair ramp metering, Transport. Res. C-Emer., 2010, 18, 489–506

Park B.B., Yun I., Ahn K., Stochastic optimization for sustainable traffic signal control, Int. J. Sustain. Transp., 2009, 3, 263–284

Park B.B., Kamarajugadda A., Development and evaluation of a stochastic traffic signal optimization method, Int. J. Sustain. Transp., 2007, 1, 193–207

Lo H.K., Szeto W.Y., Modeling advanced traveler information services: static versus dynamic paradigms, Transport. Res. B-Meth., 2004, 38, 495–515

Szeto W.Y., Lo H.K., The impact of advanced traveler information services on travel time and schedule delay costs, J. Intell. Transport. S., 2005, 9, 47–55

Article   MATH   Google Scholar  

Lo H.K., Szeto W.Y., Road pricing modeling for hypercongestion, Transport. Res. A-Pol., 2005, 39, 705–722

Google Scholar  

Zhong R.X., Sumalee A., Maruyama T., Dynamic marginal cost, access control, and pollution charge: a comparison of bottleneck and whole link models, J. Adv. Transport., 2011, Accepted

Ukkusuri S., Waller S., Linear programming models for the user and system optimal dynamic network design problem: formulations, comparisons and extensions, Netw. Spat. Econ., 2008, 8, 383–406

Szeto W.Y., Ghosh, B., Basu, B., O’Mahony M., Cell-based short-term traffic flow forecasting using time series modelling, Transport. Eng.-J. Asce., 2009, 135, 658–667

Abdelghany A.F., Adbelghany K.F., Mahmassani H.S., Murray P.M., Dynamic traffic assignment in design and evaluation of high-occupancy toll lanes, Transport. Res. Rec., 2000, 1733, 39–48

Yagar S., Dynamic traffic assignment by individual path minimization and queuing, Transport. Res., 1971, 5, 179–196

Cascetta E., Cantarella G.E., Modelling dynamics in transportation networks: state of the art and future developments, Simulat. Pract. Theory., 1993, 15, 65–91

Peeta S., Ziliaskopoulos A.K., Foundations of dynamic traffic assignment: the past, the present and the future, Netw. Spat. Econ., 2001, 1, 233–265

Boyce D., Lee D.H., Ran B., Analytical models of the dynamic traffic assignment problem, Netw. Spat. Econ., 2001, 1, 377–390

Szeto W.Y., Lo H.K., Dynamic traffic assignment: review and future, Information Technology, 2005, 5, 85–100

Szeto W.Y., Lo H.K., Properties of dynamic traffic assignment with physical queues, J. East Asia Soc. Transport. Stud., 2005, 6, 2108–2123

Mun J.S., Traffic performance models for dynamic traffic assignment: an assessment of existing models, Transport. Rev., 2007, 27, 231–249

Jeihani M., A review of dynamic traffic assignment computer packages, J. Transport. Res. Forum, 2007, 46, 35–46

Szeto W.Y., Cell-based dynamic equilibrium models, dynamic traffic assignment and signal control, in: Dynamic route guidance and traffic control, complex social, Economic and Engineered Networks Series, Ukkusuri S., Ozbay K. (Eds.), Springer, 2011, submitted

Wardrop J., Some theoretical aspects of road traffic research, ICE Proceedings: Part II, Engineering Divisions, 1952, 1, 325–362

Oppenheim N., Urban travel demand modelling: from individual choice to general equilibrium, John Wiley & Sons, USA, 1995

Daganzo C.F., Sheffi Y., On stochastic models of traffic assignment, Transport. Sci., 1977, 11, 253–274

Hall R.W., Travel outcome and performance: the effect of uncertainty on accessibility, Transport. Res. BMeth., 1993, 17, 275–290

Lo H.K., Luo X.W., Siu B.W.Y., Degradable transport network: travel time budget of travellers with heterogeneous risk aversion, Transport. Res. B-Meth., 2006, 40, 792–806

Uchida T., Iida Y., Risk assignment: a new traffic assignment model considering the risk travel time variation, In: Daganzo C.F. (Ed.), Proceedings of the 12 th International Symposium on Transportation and Traffic Theory (July 21–23, 1993, Berkeley, CA, USA), Elsevier, Amsterdam, 1993, 89–105

Jackson W.B., Jucker J.V., An empirical study of travel time variability and travel choice behavior, Transport. Sci., 1982, 16, 460–475

Brastow W.C., Jucker J.V., Use of a mean variance criterion for traffic assignment, unpublished manuscript, Department of Industrial Engineering, Stanford University, 1977

Szeto W.Y., Solayappan M., Jiang Y., Reliabilitybased transit assignment for congested stochastic transit networks, Comput.-Aided Civ. Inf., 2011, 26, 311–326

Chen A., Zhou Z., The α -reliable mean-excess traffic equilibrium model with stochastic travel times, Transport. Res. B-Meth., 2010, 44, 493–513

Franklin J.P., Karlstrom A., Travel time reliability for Stockholm roadways: modeling the mean lateness factor, Transport. Res. Rec., 2009, 2134, 106–113

Chan K.S., Lam W.H.K., Impact of road pricing on the road network reliability, J. East Asia Soc. Transport. Stud., 2005, 6, 2060–2075

Ordóñez F., Stier-Moses N.E., Wardrop equilibria with risk-averse users, Transport. Sci., 2010, 44, 63–86

Lam T., Small K., The value of time and reliability: measurement from a value pricing experiment, Transport. Res. E-Log., 2001, 37, 231–251

Bell M.G.H., Cassir C., Risk-averse user equilibrium traffic assignment: an application of game theory, Transport. Res. B-Meth., 2002, 36, 671–681

Szeto W.Y., O’Brien L., O’Mahony M., Generalisation of the risk-averse traffic assignment, In: Bell M.G.H., Heydecker B.G., Allsop R.E. (Eds.), Proceedings of 17 th International Symposium on Transportation and Traffic Theory (July 23–25, 2007, London, UK), Emerald Group Publishing Limited, 2007, 127–153

Szeto W.Y., O’Brien L., O’Mahony M., Measuring network reliability by considering paradoxes: the multiple network demon approach, Transport. Res. Rec., 2009, 2090, 42–50

Szeto W.Y., Cooperative game approaches to measuring network reliability considering paradoxes, Transport. Res. C-Emer., 2011, 11, 229–241

Szeto W.Y., O’Brien L., O’Mahony M., Risk-averse traffic assignment with elastic demand: NCP formulation and solution method for assessing performance reliability, Netw. Spat. Econ., 2006, 6, 313–332

Article   MATH   MathSciNet   Google Scholar  

Ordóñez F., Stier-Moses N.E., Robust Wardrop equilibrium, Netw. Control. Optim., 2007, 4465, 247–256

Zhang C., Chen X., Sumalee A., Robust Wardrop’s user equilibrium assignment under stochastic demand and supply: expected residual minimization approach, Transport. Res. B-Meth., 2011, 45, 534–552

Tversky A., Kahneman D., Advances in prospect theory: cumulative representation of uncertainty, J. Risk Uncertainty, 1992, 5, 297–323

Kahneman D., Tversky A., Prospect theory: an analysis of decisions under risk, Econometrica, 1979, 47, 263–291

Thaler R.H., Tversky A., Kahneman D., Schwartz A., The effect of myopia and loss aversion on risk taking: an experimental test, Quart. J. Econ., 1997, 112, 647–661

Camerer C.F., Ho T.H., Violations of the betweenness axiom and nonlinearity in probability, J. Risk Uncertainty., 1994, 8, 167–196

Avineri E., Prashker J.N., Violations of expected utility theory in route-choice stated preferences: certainty effect and inflating of small probabilities, Transport. Res. Rec., 2004, 1894, 222–229

Avineri E., The effect of reference point on stochastic network equilibrium, Transport. Sci., 2006, 40, 409–420

Mirchandani P., Soroush H., Generalized traffic equilibrium with probabilistic travel times and perceptions, Transport. Sci., 1987, 21, 133–152

Shao H., Lam W.H.K., Tam M.L., A reliability-based stochastic traffic assignment model for network with multiple user classes under uncertainty in demand, Netw. Spat. Econ., 2006, 6, 173–204

Chen A., Zhou Z., A stochastic α -reliable mean-excess traffic equilibrium model with probabilistic travel times and perception errors, In: Lam W.H.K., Wong S.C., Lo H.K. (Eds.), Proceedings of 18 th International Symposium on Transportation and Traffic Theory 2009: Golden Jubilee (July 16–18, 2009), Springer, 2009, 117–145

Chu Y.L., Work departure time analysis using dogit ordered generalized extreme value model, Transport. Res. Rec., 2009, 2132, 42–49

Jou R.C., Kitamura R., Weng M.C., Chen C.C., Dynamic commuter departure time choice under uncertainty, Transport. Res. A-Pol., 2008, 42, 774–783

Lemp J.D., Kockelman K.M., Empirical investigation of continuous logit for departure time choice with Bayesian methods, Transport. Res. Rec., 2010, 2165, 59–68

Lemp J.D., Kockelman K.M., Damien P., The continuous cross-nested logit model: Formulation and application for departure time choice, Transport. Res. B-Meth., 2010, 44, 646–661

Friesz T.L., Luque F.J., Tobin R.L., Wie B.W., Dynamic network traffic assignment considered as a continuous time optimal control problem, Oper. Res., 1989, 37, 893–901

Vickrey W.S., Congestion theory and transport investment, Am. Econ. Rev., 1969, 59, 251–261

Mahmassani H.S., Herman R., Dynamic user equilibrium departure time and route choice on an idealized traffic arterials, Transport. Sci., 1984, 18, 362–384

Merchant D.K., Nemhauser G.L., A model and an algorithm for the dynamic traffic assignment problem, Transport. Sci., 1978, 12, 183–199

Small, K.A., The scheduling of consumer activities: work trips, Am. Econ. Rev., 1982, 72, 467–479

Ran B., Boyce D.E., Modelling dynamic transportation networks: an intelligent transportation system oriented approach, 2 nd rev. ed., Springer, Berlin, 1996

Vythoulkas P.C., Two models for predicting dynamic stochastic equilibria in urban transportation networks, In: Koshi M. (Ed.), Proceedings of 11 th International Symposium on Transportation and Traffic Theory (July 18–20, Yokohama, Japan), Elsevier, 1990, 253–272

Boyce D.E., Ran B., Li I.Y., Considering travellers’ risk-taking behavior in dynamic traffic assignment, in: Bell M.G.H., Transportation networks: recent methodological advances, Elsevier, Oxford, 1999

Szeto W.Y., Jiang Y, Sumalee A., A cell-based model for multi-class doubly stochastic dynamic traffic assignment, Comput-Aided Civ. Inf., 2011, 26, 595–611

Simon H.A., Models of bounded rationality (Vol. 3), The MIT Press, Cambridge, 1997

Simon H.A., A behavioral model of rational choice, Quart. J. Econ., 1955, 69, 99–118

Simon H.A., Models of Man, Wiley, New York, 1957

MATH   Google Scholar  

Mahmassani H.S., Chang G.L., On boundedly-rational user equilibrium in transportation systems, Transport. Sci., 1987, 21, 89–99

Szeto W.Y., Lo H.K., Dynamic traffic assignment: properties and extensions, Transportmetrica, 2006, 2, 31–52

Lou Y., Yin Y., Lawphongpanich S., Robust congestion pricing under boundedly rational user equilibrium, Transport. Res. B-Meth., 2010, 44, 15–28

zeto W.Y., Lo H.K., A cell-based simultaneous route and departure time choice model with elastic demand, Transport. Res. B-Meth., 2004, 38, 593–612

Pel A.J., Bliemer M.C.J., Hoogendoorn S.P., Hybrid route choice modeling in dynamic traffic assignment, Transport. Res. Rec., 2009, 2091, 100–107

Kuwahara M., Akamatsu T., Decomposition of the reactive assignments with queues for many-to-many origin-destination pattern, Transport. Res. B-Meth., 1997, 31, 1–10

Ben-akiva M., De Palma A., Kaysi, I., Dynamic network models and driver information system, Transport. Res. A-Pol., 1991, 25, 251–266

Sumalee A., Zhong, R.X., Pan, T.L., Szeto, W.Y., Stochastic cell transmission model (SCTM): a stochastic dynamic traffic model for traffic state surveillance and assignment, Transport. Res. B-Meth., 2011, 45, 507–533

Lo H.K., Szeto W.Y., A cell-based dynamic traffic assignment model: formulation and properties, Math. Comput. Model., 2002, 35, 849–865

Tong C.O., Wong S.C., A predictive dynamic traffic assignment model in congested capacity-constrained road networks, Transport. Res. B-Meth., 2000, 34, 625–644

Horowitz J.L., The stability of stochastic equilibrium in a two-link transportation network, Transport. Res. B-Meth., 1984, 18, 13–28

Cascetta E., A stochastic process approach to the analysis of temporal dynamics in transportation networks, Transport. Res. B-Meth., 1989, 23, 1–17

Chang G.L., Mahmassani H., Travel time prediction and departure time adjustment behavior dynamics in a congested traffic system, Transport. Res. B-Meth., 1988, 22, 217–232

Lam W.H.K., Huang H.J., Dynamic user optimal traffic assignment model for many to one travel demand, Transport. Res. B-Meth., 1995, 29, 243–259

Ran B., Boyce D., A link-based variational inequality formulation of ideal dynamic user-optimal route choice problem, Transport. Res. C-Emer., 1996, 4, 1–12

Carey M., Optimal time-varying flows on congested network, Oper. Res., 1987, 35, 58–69

Carey M., Srinivasan A., Congested network flows: time-varying demands and start-time policies, Eur. J. Oper. Res., 1988, 36, 227–240

Lo H.K., Szeto W.Y., A cell-based variational inequality formulation of the dynamic user optimal assignment problem, Transport. Res. B-Meth., 2002, 36, 421–443

Szeto W.Y., The enhanced lagged cell transmission model for dynamic traffic assignment, Transport. Res. Rec., 2008, 2085, 76–85

Lo H.K., Ran B., Hongola B., Multiclass dynamic traffic assignment model: formulation and computational experiences, Transport. Res. Rec., 1996, 1537, 74–82

Liu Y.H., Mahmassani H.S., Dynamic aspects of commuter decisions under advanced traveler information systems — modeling framework and experimental results, Transport. Res. Rec., 1998, 1645, 111–119

Ben-Akiva M., Cuneo D., Hasan M., Jha M., et al., Evaluation of freeway control using a microscopic simulation laboratory, Transport. Res. C-Emer., 2003, 11, 29–50

Jiang Y.Q., Wong S.C., Ho H.W., Zhang P., et al., A dynamic traffic assignment model for a continuum transportation system, Transport. Res. B-Meth., 2011, 45, 343–363

Hughes R.L., A continuum theory for the flow of pedestrians, Transport. Res. B-Meth., 2002, 36, 507–535

Hoogendoorn S.P., Bovy P.H.L., Dynamic useroptimal assignment in continuous time and space, Transport. Res. B-Meth., 2004, 38, 571–592

Hoogendoorn S.P., Bovy P.H.L., Pedestrian routechoice and activity scheduling theory and models, Transport. Res. B-Meth., 2004, 38, 169–190

Xia Y., Wong S.C., Zhang M., Shu C.W., et al., An efficient discontinuous Galerkin method on triangular meshes for a pedestrian flow model, Int. J. Numer. Meth. Eng., 2008, 76, 337–350

Huang L., Wong S.C., Zhang M.P., Shu C.W., et al., Revisiting Hughes’ dynamic continuum model for pedestrian flow and the development of an efficient solution algorithm. Transport. Res. B-Meth., 2009, 43, 127–141

Huang L., Xia Y., Wong S.C., Shu C.W., et al., Dynamic continuum model for bi-directional pedestrian flows, P. I. Civil Eng.: Eng. Comput. Mec., 2009, 162, 67–75

Xia Y., Wong S.C., Shu C.W., Dynamic continuum pedestrian flow model with memory effect, Phys. Rev. E, 2009, 79, 066113

Jiang Y.Q., Xiong T., Wong S.C., Shu C.W., et al., A reactive dynamic continuum user equilibrium model for bi-directional pedestrian flows, Acta. Math. Sci., 2009, 29, 1541–1555

MATH   MathSciNet   Google Scholar  

Guo R.Y., Huang H.J., Wong S.C., Collection, spillback, and dissipation in pedestrian evacuation: a network-based method, Transport. Res. B-Meth., 2011, 45, 490–506

Xiong T., Zhang M., Shu C.W., Wong S.C., et al., Highorder computational scheme for a dynamic continuum model for bi-directional pedestrian flows, Comput.—Aided Civ. Inf., 2011, 26, 298–310

Nagurney A., Network economics: a variational inequality approach, Kluwer Academic Publishers, Norwell, Massachusetts, 1993

Book   MATH   Google Scholar  

Chow A.H.F., Properties of system optimal traffic assignment with departure time choice and its solution method, Transport. Res. B-Meth., 2009, 43, 325–344

Chow A.H.F., Dynamic system optimal traffic assignment — a state-dependent control theoretic approach, Transportmetrica, 2009, 5, 85–106

Long J.C., Szeto W.Y., Gao Z.Y., Discretised link travel time models based on cumulative flows: formulations and properties, Transport. Res. B-Meth., 2011, 45, 232–254

Download references

Author information

Authors and affiliations.

Department of Civil Engineering, The University of Hong Kong, Hong Kong, China

W. Y. Szeto & S. C. Wong

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to S. C. Wong .

About this article

Szeto, W.Y., Wong, S.C. Dynamic traffic assignment: model classifications and recent advances in travel choice principles. cent.eur.j.eng 2 , 1–18 (2012). https://doi.org/10.2478/s13531-011-0057-y

Download citation

Received : 30 May 2011

Accepted : 02 November 2011

Published : 21 November 2011

Issue Date : March 2012

DOI : https://doi.org/10.2478/s13531-011-0057-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Dynamic traffic assignment
• User equilibrium
• Route choice
• Departure time choice
• Find a journal
• Publish with us
• Track your research

Dynamic Traffic Assignment

Early Experiences

Current Practices

Research Needs

Page categories

Activity Based Models

Network Assignment

Topic Circles

More pages in this category:

(opens new window) is a hot topic in travel forecasting.

# Background

Traditional user equilibrium highway assignment models predict the effects of congestion and the routing changes of traffic as a result of that congestion. They neglect, however, many of the details of real-world traffic operations, such as queuing, shock waves, and signalization. Currently, it is common practice to feed the results of user equilibrium traffic assignments into dynamic network models as a mechanism for evaluating these policies. The simulation models themselves, however, do not predict the routing of traffic, and therefore are unable to account for re-routing owing to changes in congestion levels or policy, and can be inconsistent with the routes determined by the assignment. Dynamic network models overcome this dichotomy by combining a time-dependent shortest path algorithm with some type of simulation (often meso or macroscopic) of link travel times and delay. In doing so it allows added reality and consistency in the assignment step, as well as the ability to evaluate policies designed to improve traffic operations. These are some of the main benefits of dynamic network models .

DTA models can generally be classified by how they model link or intersection delay. Analytical DTA models treat it in the same manner as static equilibrium assignment models, with no explicit representation of signals. Link capacity functions, often similar or identical to those used in static assignment, are used to calculate link travel times. Analytical models have been widely used in research and for real-time control system applications. Simulation-based DTA models include explicit representation of traffic control devices. Such models require detailed signal parameters to include phasing, cycle length, and offsets for each signal in the network. Delay is calculated for each approach, with vehicles moving from one link to the next only if available downstream capacity is available. The underlying traffic model is often different, but at the network level such models behave in a similar fashion.

Demand is specified in the form of origin–destination matrices for short time intervals, typically 15 minutes each. Trips are typically randomly loaded onto the network during each time interval. As with traffic microsimulation models, adequate downstream capacity must be present to load the trips onto the network. The shortest paths through time and space are found for each origin–destination pair, and flows loaded to these paths. A generalized flowchart of the process is shown below.

As with static assignment models, the process shown above is iteratively solved until a stable solution is reached. The memory and computing requirements of DTA, however, are orders of magnitude larger than for static assignment, reducing the number of iterations and paths that can be kept in memory. Instead of a single time period, as with static assignment, DTA models must store data for each time interval as well. A three-hour static assignment would involve only one time interval. A DTA model of the same period, however, might require 12 intervals, each 15 minutes in duration. These are all in addition to the memory requirements imposed by the number of user classes and zones.

# Early Experiences

Research into DTA dates back several decades, but was largely limited to academics working on its formulation and theoretical aspects. DTA overcomes the limitations of static assignment models, although at the cost of increased data requirements and computational burden. Moreover, software platforms capable of solving the DTA problem for large urban systems and experience in their use are recent developments.

(opens new window) has been successfully applied to a large subarea of Calgary and to analyses of the Rue Notre-Dame in Montreal. Although user group presentations of both applications have been made, and reported very encouraging results, the work is currently unpublished and inaccessible except through contact with the developers.

(opens new window) . The network from the Atlanta Regional Commission (ARC) regional travel model formed the starting point for the DTA network. Intersections were coded, centroid connectors were re-defined, and network coding errors were corrected. A signal synthesizer derived locally optimal timing parameters for more than 2,200 signalized intersections in the network. Trip matrices from the ARC model were divided into 15-minute intervals for the specification of demand. Approximately 40 runs of the model were required to diagnose coding and software errors. Unfortunately, the execution time for the model was approximately one week per run. The resulting model eventually validated well to observed conditions; however, the length of time required to render it operational and the run time required prevented it from being used in studies as originally intended. Subsequent work by the developer has resulted in substantial reductions in run time, but this remains a significant issue that must be overcome before such models can be more widely used.

# Current Practices

# research needs.

A number of cities are currently testing DTA models, but are not far enough along in their work to share even preliminary results. At least a dozen such cases are known to be in varying stages of planning or execution, suggesting that the use of DTA models in planning applications is about to expand dramatically. However, in addition to the issue of long run times, a number of other issues must be addressed before such models are likely to be widely adopted:

• Criteria for the validation of such models have not been widely accepted. The paucity of traffic counts in most urban areas, and especially at 15, 30, or 60 minute intervals, is a significant barrier to definitive assessment of these models.

← Network assignment Land Use-Transport Modeling →

This site uses cookies to learn which topics interest our readers.

• History of cooperation
• Areas of cooperation
• Procurement policy
• Useful links
• Becoming a supplier
• Procurement
• Rosatom newsletter

© 2008–2024Valtiollinen Rosatom-ydinvoimakonserni

• Rosatom Global presence
• Rosatom in region
• For suppliers
• Preventing corruption
• Press centre

Rosatom Starts Life Tests of Third-Generation VVER-440 Nuclear Fuel

• 16 June, 2020 / 13:00

This site uses cookies. By continuing your navigation, you accept the use of cookies. For more information, or to manage or to change the cookies parameters on your computer, read our Cookies Policy. Learn more