Microscopic Traffic Simulation Model-Based Optimization Approach for the Contraflow Lane Configuration Problem
Publication: Journal of Transportation Engineering
Volume 134, Issue 1
Abstract
This paper addresses the optimal contraflow lane configuration problem arising in the contraflow lane control strategy that has been implemented successfully in practice. The problem is formulated as a bilevel programming model in which the upper level problem is a binary integer programming formulation that aims to minimize the total travel time of a study area, while the lower level problem is a microscopic traffic simulation model that can simulate the dynamic reaction of the drivers resulting from a contraflow lane configuration scheme. A microscopic traffic simulation model is adopted in this study because it is easily handled by traffic engineers. Such an adoption results in inexistence of analytical expression of the objective function in the upper level problem. Accordingly, some conventional analytical solution methods for solving integer programming problems are no longer available for the proposed model. Therefore, this paper develops a variation of genetic algorithm that embeds with the microscopic traffic simulation model as well as a chromosome repairing procedure to find an optimal contraflow lane configuration solution. A case study in Singapore is carried out to evaluate the proposed methodology, in which PARAMICS as the microscopic traffic simulation model is applied.
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Acknowledgments
The writers would like to express their greatest appreciation to Associate Professor Der-Horng Lee and Dr. P. Chandrasekar from the Department of Civil Engineering, National University of Singapore for their valuable comments on microscopic traffic simulation modeling.
References
A1-Kaisy, A. F., and Kerestes, E. (2005). “Evaluation of effectiveness of single-lane two-way traffic control at maintenance and reconstruction.” Proc., 84th Annual Meeting of the Transportation Research Board (CD-ROM), Washington, D.C.
Barcelo, J., Codina, E., Casas, J., Ferrer, J. L., and Garcia, D. (2004). “Microscopic traffic simulation: A tool for the design, analysis and evaluation of intelligent transport systems.” J. Intell. Robotic Syst., 41(2–3), 173–203.
Ben-Akiva, M. E., Bierlaire, M., and Koutsopoulos, H. N. (2002). “Real-time simulation of traffic demand-supply interactions with DynaMIT.” Transportation and network analysis: Current trends. Miscellenea in honor of Michael Florian, M. Gendreau and P. Marcotte, eds., Kluwer, Boston/Dordrecht/London, 19–36.
Boxill, S. A., and Yu, L. (2000). “An evaluation of traffic simulation models for supporting ITS development.” Technical Rep. No. SWUTC/00/167602-1, Center for Transportation Training and Research, Texas Southern Univ.
Carey, M., and Ge, Y. E. (2005). “Alternative conditions for a well-behaved travel time model.” Transp. Sci., 39(3), 417–428.
Cheu, R. L., Wang, Y., and Fwa, T. F. (2004). “Genetic algorithm-simulation methodology for pavement maintenance scheduling.” Comput. Aided Civ. Infrastruct. Eng., 19(6), 446–455.
Chu, L. Y., Liu, H. X., Recker, E., and Zhang, H. M. (2004). “Performance evaluation of adaptive ramp-metering algorithms using microscopic traffic simulation model.” J. Transp. Eng., 130(3), 330–338.
Drezner, Z., and Wesolowsky, G. (1997). “Selecting an optimum configuration of one-way and two-way routes.” Transp. Sci., 31(4), 386–394.
Friesz, T. L., Bernstein, D., Smith, T. E., Robin, R. L., and Wie, B. W. (1993). “A variational inequality formulation of the dynamic user equilibrium problem.” Oper. Res., 41, 179–191.
Glover, F. (1986). “Future paths for integer programming and links to an artificial intelligence.” Comput. Oper. Res., 13, 533–549.
Holland, J. H. (1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, Mich.
Jayakrishnan, R., Mahmassani, H. S., and Hu, T. Y. (1994). “An evaluation tool for advanced traffic information and management systems in urban networks.” Transp. Res., Part C: Emerg. Technol., 2, 129–147.
Kirkpatrick, S., Jr., Gelatt, C. D., and Vecchi, M. P. (1983). “Optimization by simulated annealing.” Science, 220, 671–680.
Lee, D.-H., Wang, H., Cheu, R. L., and Teo, S. H. (2004). “Taxi dispatch system based on current demands and real-time traffic condition.” Transportation Research Record. 1882, Transportation Research Board, Washington, D.C., 193–200.
Lehmuskoski, V., Niittymaki, J., and Silfverberg, B. (2000). “Microscopic simulation on high-class roads—Enhancement of environmental and driving dynamics: Practical applications.” Transportation Research Record. 1706, Transportation Research Board, Washington, D.C., 73–81.
Lin, W. H., and Lo, H. K. (2000). “Are the objective and solutions of dynamic user-equilibrium models always consistent?” Transp. Res., Part A: Policy Pract., 34, 421–443.
Lu, H., Ma, W. T., and Jayakrishnan, R. (2005). “Distributed modeling framework for large-scale microscopic simulation.” Proc., 84th Annual Meeting of the Transportation Research Board (CD-ROM), Washington, D.C.
Ma, W., Cheu, R. L., and Lee, D.-H. (2004). “Scheduling of lane closures using genetic algorithms with traffic assignments and distributed simulations.” J. Transp. Eng., 130(3), 322–329.
Meng, Q., Lee, D-H., Yang, H., and Huang, H.-J. (2004). “Transportation network optimization problems with stochastic user equilibrium constraints.” Transportation Research Record. 1882, Transportation Research Board, Washington, D.C., 113–119.
Meng, Q., Yang, H., and Bell, M. G. H. (2001). “An equivalent continuously differentiable model and a locally convergent algorithm for the continuous network design problem.” Transp. Res., Part B: Methodol., 35, 83–105.
Migdalas, A. (1995). “Bilevel programming in traffic planning: Models, methods and challenge.” J. Global Optim., 7, 381–405.
Ni, D., Leonard, J. D., Guin, A., and Williams, B. M. (2004). “Systematic approach for validating traffic simulation models.” Transportation Research Record. 1876, Transportation Research Board, Washington, D.C., 20–31.
Ran, B., and Boyce, D. (1996). Modeling dynamic transportation networks: An intelligent transportation system oriented approach, Springer, Berlin.
Shimizu, K., Ishizuka, Y., and Bard, F. (1997). Nondifferentiable and two-level mathematical programming, Kluwer, Boston.
Theodoulou, G., and Wolshon, B. (2004). “Alternative methods to increase the effectiveness of freeway contraflow evacuation.” Transportation Research Record. 1865, Transportation Research Board, Washington, D.C., 48–56.
Xue, D., and Dong, D. (2002). “An intelligent contraflow control method for real-time optimal traffic scheduling using artificial neural network, fuzzy pattern recognition, and optimization.” IEEE Trans. Control Syst. Technol., 8(1), 183–191.
Yang, D. (1992). “The optimal control of Massey tunnel contraflow operation.” Highway Engineering Branch, Ministry of Transportation and Highways of British Columbia.
Yang, H., and Bell, M. G. H. (1998). “A capacity paradox in network design and how to avoid it.” Transp. Res., Part A: Policy Pract., 32, 539–545.
Zhou, W. W., Livolsi, P., Miska, E., Zhang, H., Wu, J., and Yang, D. (1993). “An intelligent traffic responsive contraflow lane control system.” Proc., IEEE-IEE Vehicle Navigation and Information Systems Conf., Ottawa, Canada, 174–181.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 134 • Issue 1 • January 2008
Pages: 41 - 49
Copyright
© 2008 ASCE.
History
Received: Jan 11, 2006
Accepted: May 25, 2007
Published online: Jan 1, 2008
Published in print: Jan 2008
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