Simulation model based on Monte Carlo method for traffic assignment in local area road network

Abstract

For a local area road network, the available traffic data of traveling are the flow volumes in the key intersections, not the complete OD matrix. Considering the circumstance characteristic and the data availability of a local area road network, a new model for traffic assignment based on Monte Carlo simulation of intersection turning movement is provided in this paper. For good stability in temporal sequence, turning ratio is adopted as the important parameter of this model. The formulation for local area road network assignment problems is proposed on the assumption of random turning behavior. The traffic assignment model based on the Monte Carlo method has been used in traffic analysis for an actual urban road network. The results comparing surveying traffic flow data and determining flow data by the previous model verify the applicability and validity of the proposed methodology.

Keywords

traffic assignment / local area road network / turning ratio / Monte Carlo method

References

[1] Wardrop J G. Some theoretical aspects of road traffic research. In: Proceedings of ICE, Part II. 1952, 1: 325-378

[2] Bechmann M, Mcguire C B, Winsten C B. Studies in the Economics of Transportation. CT: Yale University Press, 1956

[3] LeBlanc L J, Morlok E K, Piersskalla W P. An efficient approach to solving the road network equilibrium traffic assignment problem. Transportation Research, 1975, 9: 309-318

[4] Aashitiani H Z, Magnanti T L. Equilibrium on a congested transportation network. SIAM Journal of Algebraic and Discrete Methods, 1981, 2: 213-226

[5] Fernandez L J E, Freisz T L. Equilibrium predictions in transportation markets: the state of the art. Transportation Research, 1983, 17B: 155-172

[6] Carey M A. Optimal time-varying flows on congested networks. Operations Research, 1987, 35 (1): 58-69

[7] Ran B, Boyce D E, Leblanc L J. A new c1ass of instantaneous dynamic user—optimal traffic assignment models. Operations Research, 1993, 41(1): 192-202

[8] Florian M, Wu J H, He S. A multi-class multi-mode variable demand network equilibrium model with hierarchical Logit structures. In: Proceedings of Seminar C held at the PTRC Transport and Planning Summer Annual Meeting, University of Sussex, England. 2000, 211-224

[9] Poole M R. Technical Report #11: Allocation Type Approach to Estimation of Travel for Small Urban Areas. NCDOT Statewide Planning Branch, 1989

[10] Anderson M D. Performance of single internal trip purpose methodology to model smaller urban area traffic. Journal of Urban Planning and Development, 2002, 128(1): 1-17

[11] McDonnell J T. Synthesized Through Trip Table for Small Urban Areas. NCDOT Statewide Planning Branch, 1991

[12] Sun Lijun, Yuan Wenping, Cai Xiaoyu. Traffic Impact Analysis and Management Method for Regional Development. Shanghai.2005 (in Chinese)

[13] Bell M G H. The real time estimation of origin-destination flows in the presence of platoon dispersion. Transportation Research Part B, 1991, 25 (2/3): 115-125

[14] Lan C J, Davis G A. Real-time estimation of turning movement proportions from partial counts on urban networks. Transportation Research Part C, 1999, 7 (5): 305-327

[15] Nobe S. On-line estimation of traffic split parameters based on lane counts. AZ: The University of Arizona, 2002

[16] Zhang L, McHale G, Zhang Y. Modeling and validating CORSIM freeway origin-destination volumes. Transportation Research Record, 2003, 1856: 135-142

[17] Muthuswamy S, Levinson D, Michalopoulos P, Davis G. Report number CTS 04-11: Improving the estimation of travel demand for traffic simulation. MN: Department of Civil Engineering, University of Minnesota, 2005

[18] Geng Y J,Du Y C,Sun L J. Research on the Traffic Characteristics of Ground-Level Roads in Shanghai Based on ATMS Data. In: Proceedings of the 11th International Conference of Hong Kong Society for Transportation Studies. 2006, 12: 751-757

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Higher Education Press and Springer-Verlag Berlin Heidelberg