Using the Genetic Algorithm for Optimization of the Integrated Urban Transportation Systems
Keywords:Multi- Demsional Transport, Genetic Algorithms, Pricing, Investment, Regulation
Improving the public transportation problems should rely on integrated multidimensional transport policies which can soften the demand of infrastructure investment. However, it would be very difficult to fully consider the multi-dimensional transport polices in planning framework because there would be too many possible policy combinations to be evaluated. So, this study attempts to develop an analytic framework for evaluating urban integrated transport policies comprehensively, including strategies of investment, pricing, management and regulation. To deal with the difficulty of too many policy combinations, genetic algorithms will be used to search for the optimal strategy combination for integrated transport strategy. Finally, the relationship between quantified objectives, policy combinations, and assessment performances would be analyzed using the proposed model.
Chen, Y. W., Wang, S. E. and Chiang, Y. S. (2005) ‘Performance Evaluation framework of an Integrated goal-related transport policy’, Proceedings of the Eastern Asia Society for Transportation Studies (EASTS’05), 5:1, 450-460.
Goldberg, D. E. (1989) Genetic algorithms in search, optimization and machine learning, AddisonWesley Longman Publishing Co., Inc. Boston, MA, USA.
Jones, A. D., May, A. D., and Wenban-Smith, A. (1990) ‘Integrated transport studies: lessons from the Birmingham study’, Traffic Engineering and Control, 31:11, 572-578.
Jones, P., Lucas, K., and Whittles, M. (2003) Evaluating and implementing transport measures in a wider policy context: the ‘Civilising Cities’ initiative, Transport Policy, 10:3, 209-221.
Holland, J. H. (1975) Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor.
KCTB (2004) Kaohsiung Transport Policy White Book, Kaohsiung City Transportation Bureau.
Matthews, B., Jopson, A., and May, A.D. (2002) Developing a Method for the Assessment of Evidence on the Impacts of Transport Policy Instruments, Proceedings of the European Transport Conference, London, Homerton College.
May, A. D. and Roberts, M. (1995) The design of integrated transport strategies, Transport Policy, 2:2, 97-105.
Michalewicz, Z. (1999) Genetic Algorithms + Data Structure = Evolution Programs(3rd edn.), New York , Springer-Verlag Berlin Heidelberg.
Potter, S. and Skinner, M. J. (2000) ‘On transport integration: a contribution to better understanding’, Futures, 32:1, 275-287.
Taleghani, M. (2006) ‘Development and deployment of public transport policy and planning in Iran’, Transportation, 33:2, 153-170.
Timms, P.M., May, A.D., and Shepherd, S.P. (2002) ‘The sensitivity of optimal transport strategies to specification of objectives’, Transportation Research Part A, 36:5, 383-401.
U.S.A. (1986) Urban transport, Washington D. C., World Bank.
U.S.A. (1996) Sustainable Transport: Priorities for Policy Reform, Washington D. C., World Bank.
U.S.A. (2002) Cities on the move: A World Bank urban transport strategy review, Washington D. C., World Bank.
Wong, S. C. and Lam, W. H. K. (2006) ‘Planning and policy of public transportation systems in Asia’, Transportation, 33:2, 111-113.