Skip to main content
Log in

DTA2012 Symposium: Combining Disaggregate Route Choice Estimation with Aggregate Calibration of a Dynamic Traffic Assignment Model

  • Published:
Networks and Spatial Economics Aims and scope Submit manuscript

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.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

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

    Article  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Ben-Akiva M, Lerman S (1985) Discrete choice analysis. MIT Press, Cambridge

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Bierlaire M, Frejinger E (2008) Route choice modeling with network-free data. Transp Res Part C 16:187–198

    Article  Google Scholar 

  • 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

    Book  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Frejinger E, Bierlaire M, Ben-Akiva M (2009) Sampling of alternatives for route choice modeling. Transp Res Part B 43(10):984–994

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Spall JC (1998) Implementation of the simultaneous perturbation algorithm for stochastic approximation. IEEE Trans Aerosp Electron Syst 34:817–823

    Article  ADS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Train K (2003) Discrete choice methods with simulation. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Yai T, Iwakura S, Morichi S (1997) Multinomial probit with structured covariance for route choice behavior. Transp Res Part B 31(3):195–207

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Song Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11067-014-9232-z

Keywords

Navigation