Using the Genetic Algorithm for Optimization of the Integrated Urban Transportation Systems

Abstract

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.