Advertisement

Traffic Simulation with DynaMIT

  • Moshe Ben-Akiva
  • Haris N. Koutsopoulos
  • Constantinos Antoniou
  • Ramachandran Balakrishna
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 145)

Abstract

DynaMIT (Dynamic Network Assignment for the Management of Information to Travelers) is a dynamic traffic assignment model system that estimates and predicts traffic. DynaMIT is also a real-time system for decision support at traffic management centers for generation of predictive traffic information. A planning version also exists. DynaMIT captures the dynamic performance of the network (e.g., lane-based queuing and spillback effects), travel behavior, its sensitivity to traffic conditions and available traffic information, and consistency between demand and supply. DynaMIT consists of a demand simulator, a supply simulator, and algorithms that capture demand and supply interactions. Methodologies for the online and offline estimation of OD flows and the offline and online calibration of various inputs and parameters (such as network performance parameters) have been developed as well. Several case studies from the United States, Europe, and Asia are discussed, and a distributed version of DynaMIT is also presented.

Keywords

Extend Kalman Filter Route Choice Simultaneous Perturbation Stochastic Approximation Dynamic Traffic Assignment Route Guidance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Antoniou C, Ben-Akiva M, Bierlaire M, Mishalani R (1997) Demand simulation for dynamic traffic assignment. Proceedings of the 8th IFAC symposium on transportation systems, Chania, GreeceGoogle Scholar
  2. Antoniou C, Ben-Akiva, Koutsopoulos HN (2007) Non-linear Kalman filtering algorithms for on-line calibration of dynamic traffic assignment models. IEEE Transactions on Intelligent Transportation Systems, vol 8. Issue 4, pp 661–670CrossRefGoogle Scholar
  3. Argonne National Laboratory (2008) MPICH2: high-performance and widely portable MPI. http://www.mcs.anl.gov/research/projects/mpich2/, Accessed 24 July 2008
  4. Ashok K, Ben-Akiva M (1993) ‘Dynamic O-D matrix estimation and prediction for real-time traffic management systems’. In: Daganzo C (ed) ‘Transportation and traffic theory’, Elsevier Science Publishing, Oxford, pp 465–484Google Scholar
  5. Ashok K, Ben-Akiva M (2000) Alternative approaches for real-time estimation and prediction of time-dependent origin-destination flows. Transportation Science, vol 34, no.1Google Scholar
  6. Balakrishna R (2006) Off-line calibration of dynamic traffic assignment models. PhD thesis, Department of Civil and Environmental Engineering, Massachusetts Institute of TechnologyGoogle Scholar
  7. Balakrishna R, Koutsopoulos HN, Ben-Akiva M (2005) “Calibration and Validation of Dynamic Traffic Assignment Systems.” Mahmassani HS (ed) 16th international symposium on transportation and traffic theory (ISTTT), Maryland, pp 407–426 (ISBN: 0-08-044680-9)Google Scholar
  8. Balakrishna R, Ben-Akiva M, Koutsopoulos HN (2007) Off-line calibration of dynamic traffic assignment: simultaneous demand and supply estimation. Tran Res Record, No. 2003, 50–58Google Scholar
  9. Balakrishna R, Wen Y, Ben-Akiva M, Antoniou C (2008) Simulation-based framework for transportation network management for emergencies. Transportation Res Record: J Trans Res Board. Number 2041, 80–88.Google Scholar
  10. Ben-Akiva M, Bierlaire M, Bottom J, Koutsopoulos HN, Mishalani RG (1997) Development of a route guidance generation system for real-time application. Proceedings of the 8th IFAC symposium on transportation systems, Chania, GreeceGoogle Scholar
  11. 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, international series in operations research and management science, vol 23. Kluwer Academic, BostonGoogle Scholar
  12. Ben-Akiva M, Bierlaire M, Koutsopoulos HN, Mishalani R (2002) ‘Real-time simulation of traffic demand-supply interactions within DynaMIT’. In: Gendreau M, Marcotte P (eds) ‘Transportation and network analysis: current trends. Miscellenea in honor of Michael Florian’, Kluwer Academic Publishers, Boston/Dordrecht/London, pp 19–36Google Scholar
  13. Ben-Akiva M, Bierlaire M (2003) Discrete choice models with applications to departure time and route choice. In: Hall R (ed) Handbook of transportation science, 2nd edn, Kluwer Academic, BostonGoogle Scholar
  14. Ben-Akiva M, Koutsopoulos HN, Walker J (2001) DynaMIT-P. Dynamic assignment model system for transportation planning. Proceedings of the 2001 world conference in transportation research (WCTR), Seoul, KoreaGoogle Scholar
  15. Bottom J, Ben-Akiva M, Bierlaire M, Chabini I, Koutsopoulos HN, Yang Q (1999) Investigation of route guidance generation issues by simulation with DynaMIT. In: Ceder A (ed) Transportation and traffic theory, proceedings of the 14th international symposium on transportation and traffic theory, Pergamon, Oxford, pp 577–600Google Scholar
  16. Cascetta E, Inaudi D, Marquis G (1993) Dynamic estimators of origin-destination matrices using traffic counts. Trans Sci 27(4):363–373CrossRefGoogle Scholar
  17. Cascetta E, Russo F, Viola FA, Vitetta A (2002) A model of route perception in urban road networks. Trans Res Part B: Methodological 36(7):577–592CrossRefGoogle Scholar
  18. Chui CK, Chen G (1999) Kalman filtering with real-time applications, Springer, New YorkGoogle Scholar
  19. Greene WH (2000) Econometric analysis, 4th edn, Prentice-Hall Inc., Upper Saddle River, New JerseyGoogle Scholar
  20. HCM (2000) Highway capacity manual. Transportation Research Board, Washington, DCGoogle Scholar
  21. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng (ASME) 82D:35–45CrossRefGoogle Scholar
  22. 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. Proceedings of the 87th annual meeting of the transportation research board, Washington, DCGoogle Scholar
  23. Spall JC (1998) Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans on Aerospace Electronic Sys 34(3):817–823CrossRefGoogle Scholar
  24. Sundaram S (2002) Development of a dynamic traffic assignment system for short-term planning applications. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  25. UC Berkeley, Caltrans (2005) Freeway performance measurement system (PEMS) 5.4. http://pems.eecs.berkeley.edu/Public. Accessed 30th Apr 2009
  26. Wen Y, Balakrishna R, Ben-Akiva M, Smith S (2007) “On-line deployment of dynamic traffic assignment: evaluations and lessons.” 11th world conference on transport research (WCTR), 24–28 June, Berkeley.Google Scholar
  27. Wen Y (2009) Scalability of dynamic traffic assignment, PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  28. Xu S (2009) Development and test of dynamic congestion pricing model. Master thesis, Massachusetts Institute of TechnologyGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Moshe Ben-Akiva
    • 1
  • Haris N. Koutsopoulos
    • 2
  • Constantinos Antoniou
    • 3
  • Ramachandran Balakrishna
    • 4
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.The Royal Institute of TechnologyStockholmSweden
  3. 3.National Technical University of AthensAthensGreece
  4. 4.Caliper CorporationNewtonUSA

Personalised recommendations