Abstract
Dynamic Traffic Assignment (DTA) models are important decision support tools for transportation planning and real-time traffic management. One of the biggest obstacles of applying DTA in large-scale networks is the calibration of model parameters, which is essential for the realistic replication of the traffic condition. This paper proposes a methodology for the simultaneous demand-supply DTA calibration based on both aggregate measurements and disaggregate route choice observations to improve the calibration accuracy. The calibration problem is formulated as a bi-level constrained optimization problem and an iterative solution algorithm is proposed. A case study in a highly congested urban area of Beijing using DynaMIT-P is conducted and the combined calibration method improves the fits to surveillance data compared to the calibration based on aggregate measurements only.
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References
Antoniou C (2004) On-line calibration for dynamic traffic assignment. PhD thesis, Massachusetts Institute of Technology
Azevedo J, Costa MS, Madeira JS, Martins EV (1993) An algorithm for the ranking of shortest paths. Eur J Oper Res 69:97–106
Balakrishna R (2006) Off-line calibration of dynamic traffic assignment models. PhD thesis, Massachusetts Institute of Technology
Balakrishna R, Koutsopoulos HN, Ben-Akiva M (2005) Calibration and validation of dynamic traffic assignment systems. In: Mahmassani HS (ed) Transportation and traffic theory: flow, dynamics and human interaction, proceedings of the 16th international symposium on transportation and traffic theory. Elsevier, University of Maryland, College Park, pp 407–426
Balakrishna R, Ben-Akiva M, Koutsopoulos HN (2007) Offline calibration of dynamic traffic assignment: simultaneous demand and- supply estimation. Transp Res Rec J Transp Res Board 2003:50–58
Balakrishna R, Wen Y, Ben-Akiva M, Antoniou C (2008) Simulation-based framework for transportation network management for emergencies. Transp Res Rec J Transp Res Board 2041:80–88
Balakrishna R, Morgan D, Slavin H, Yang Q (2009) Large-scale traffic simulation tools for planning and operations management. In: 12th IFAC symposium on transportation systems
Barcelo J, Casas J (2006) Stochastic heuristic dynamic assignment based on aimsun microscopic traffic simulator. Transp Res Rec J Transp Res Board 1964:70–80
Ben-Akiva M, Bierlaire M (1999) Discrete choice methods and their applications to short-term travel decisions. In: Hall R (ed) Handbook of transportation science. Kluwer, Dordrecht, pp 5–34
Ben-Akiva M, Lerman S (1985) Discrete choice analysis. MIT Press, Cambridge
Ben-Akiva M, Bergman M, Daly A, Ramaswamy R (1984) Modeling inter urban route choice behaviour. In: Proceeding of the 9th international symposium on transportation and traffic theory
Ben-Akiva M, Bierlaire M, Bottom J, Koutsopoulos HN, Mishalani RG (1997) Development of a route guidance generation system for real-time application. In: Proceedings of the 8th international federation of automatic control symposium on transportation systems. IFAC, Chania
Ben-Akiva M, Bierlaire M, Burton D, Koutsopoulos HN, Mishalani R (2001) Network state estimation and prediction for real-time transportation management applications. Netw Spat Econ 1:291–318
Ben-Akiva M, Bottom J, Gao S, Koutsopoulos HN, Wen Y (2007) Towards disaggregate dynamic travel forecasting models. Tsinghua Sci Technol 12(2):115–130
Ben-Akiva ME, Gao S, Wei Z, Wen Y (2012) A dynamic traffic assignment model for highly congested urban networks. Transp Res Part C 24:62–82
Bierlaire M, Frejinger E (2008) Route choice modeling with network-free data. Transp Res Part C 16:187–198
Bolduc D, Ben-Akiva M (1991) A multinomial probit formulation for large choice sets. In: Proceedings of the 6th international conference on travel behaviour
Bovy PHL, Fiorenzo-Catalano S (2006) Stochastic route choice set generation: behavioral and probabilistic foundations. In: Proceedings of the 11th international conference on travel behaviour research. Kyoto
Burrell JE (1968) Multiple route assignment and its application to capacity restraint. In: Proceeding of the fourth international symposium on the theory of traffic flow
Cascetta E (2001) Transportation systems engineering: theory and methods, applied optimization. Kluwer, Boston
Cascetta E, Nuzzolo A, Russo F, Vitetta A (1996) A modified logit route choice model overcoming path overlapping problems: specification and some calibration results for interurban networks. In: Lesort JB (ed) Proceedings of the 13th international symposium on transportation and traffic theory. Lyon
Daganzo CF, Sheffi Y (1977) On stochastic models of traffic assignment. Transp Sci 11(3):253–274
de la Barra T, Pérez B, Añez J (1993) Multidimensional path search and assignment. In: Proceedings of the 21st PTRC summer meeting, pp 307–319
Florian M, Mahut M, Tremblay N (2001) A hybrid optimization-mesoscopic simulation dynamic traffic assignment model. In: Proceeding of the nternational IEEE conference on intelligent transportation systems, Aug. 25–29. Oakland, pp 118–121
Fosgerau M, Frejinger E, Karlstrom A (2012) A logit model for the choice among infinitely many routes in a network. Technical report. Royal Institute of Technology
Frejinger E (2007) Route choice analysis: data, models, algorithms and applications. PhD thesis, Ecole Polytechnique Federale de Lausanne
Frejinger E, Bierlaire M (2007) Capturing correlation with subnetworks in route choice models. Transp Res Part B 41:363–378
Frejinger E, Bierlaire M, Ben-Akiva M (2009) Sampling of alternatives for route choice modeling. Transp Res Part B 43(10):984–994
Gao S (2005) Optimal adaptive routing and traffic assignment in stochastic time-dependent networks. PhD thesis, MIT
Hou A (2010) Using gps data in route choice analysis: case study in boston. Master’s thesis, Massachusetts Institute of Technology
Mahmassani HS (2001) Dynamic network traffic assignment and simulation methodology for advanced system management applications. Netw Spat Econ 1(3/4):267–292
Peeta S, Ziliaskopoulos AK (2001) Foundations of dynamic traffic assignment: the past, the present and the future. Netw Spat Econ 1(3/4):233–265
Prato CG (2004) Latent factors and route choice behavior. PhD thesis, Politecnico di Torio
Ramming S (2002) Network knowledge and route choice. PhD thesis, Massachusetts Institute of Technology, Cambridge
Rathi V, Antoniou C, Wen Y, Ben-Akiva M, Cusack M (2008) Assessment of the impact of dynamic prediction-based route guidance using a simulation-based, closed-loop framework.In: The 87th annual meeting of the transportation research board. DVD-ROM, Washington D.C.
Spall JC (1998) Implementation of the simultaneous perturbation algorithm for stochastic approximation. IEEE Trans Aerosp Electron Syst 34:817–823
Sundaram S, Koutsopoulos HN, Ben-Akiva M, Antoniou C, Balakrishna R (2011) Simulation-based dynamic traffic assignment for short-term planning applications. Simul Model Pract Theory 19:450–462
Train K (2003) Discrete choice methods with simulation. Cambridge University Press, Cambridge
Vaze V, Antoniou C, Wen Y, Ben-Akiva M (2009) Calibration of dynamic traffic assignment models with point-to-point traffic surveillance. Transp Res Rec J Transp Res Board 2090:1–9
Wen Y (2009) Scalability of dynamic traffic assignment. PhD thesis, Massachusetts Institute of Technology
Wen Y, Balakrishna R, Ben-Akiva M, Smith S (2006) Online deployment of Dynamic Traffic Assignment: architecture and run-time management. IEE Proc Intell Transp Syst 153(1):76–84
Yai T, Iwakura S, Morichi S (1997) Multinomial probit with structured covariance for route choice behavior. Transp Res Part B 31(3):195–207
Ziliaskopoulos AK, Waller ST, Li Y, Byram M (2004) Large-scale dynamic traffic assignment: implementation issues and computational analysis. J Transp Eng 130(5):585–593
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Ben-Akiva, M., Gao, S., Lu, L. et al. DTA2012 Symposium: Combining Disaggregate Route Choice Estimation with Aggregate Calibration of a Dynamic Traffic Assignment Model. Netw Spat Econ 15, 559–581 (2015). https://doi.org/10.1007/s11067-014-9232-z
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DOI: https://doi.org/10.1007/s11067-014-9232-z