Fundamentals of Traffic Simulation pp 363-398 | Cite as
Traffic Simulation with DynaMIT
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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 GuidanceReferences
- 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
- 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
- Argonne National Laboratory (2008) MPICH2: high-performance and widely portable MPI. http://www.mcs.anl.gov/research/projects/mpich2/, Accessed 24 July 2008
- 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
- 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
- Balakrishna R (2006) Off-line calibration of dynamic traffic assignment models. PhD thesis, Department of Civil and Environmental Engineering, Massachusetts Institute of TechnologyGoogle Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Cascetta E, Inaudi D, Marquis G (1993) Dynamic estimators of origin-destination matrices using traffic counts. Trans Sci 27(4):363–373CrossRefGoogle Scholar
- 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
- Chui CK, Chen G (1999) Kalman filtering with real-time applications, Springer, New YorkGoogle Scholar
- Greene WH (2000) Econometric analysis, 4th edn, Prentice-Hall Inc., Upper Saddle River, New JerseyGoogle Scholar
- HCM (2000) Highway capacity manual. Transportation Research Board, Washington, DCGoogle Scholar
- Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng (ASME) 82D:35–45CrossRefGoogle Scholar
- 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
- Spall JC (1998) Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans on Aerospace Electronic Sys 34(3):817–823CrossRefGoogle Scholar
- Sundaram S (2002) Development of a dynamic traffic assignment system for short-term planning applications. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
- UC Berkeley, Caltrans (2005) Freeway performance measurement system (PEMS) 5.4. http://pems.eecs.berkeley.edu/Public. Accessed 30th Apr 2009
- 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
- Wen Y (2009) Scalability of dynamic traffic assignment, PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
- Xu S (2009) Development and test of dynamic congestion pricing model. Master thesis, Massachusetts Institute of TechnologyGoogle Scholar