Multi-dimensional integrated transport policies optimization based on genetic algorithm

Abstract

This study attempts to develop an analytic framework for evaluating urban integrated transport policies comprehensively, including strategies of investment, pricing, management and regulation. In particular, to deal with the difficulty of too many policy combinations, genetic algorithms will be employed to search for the optimal strategy combination for integrated transport policy. Finally, the relationships between quantified objectives, policy combinations, and assessment performances would be analyzed using the proposed model in this study. The results can also provide a reference to decision makers when drafting urban integrated transport policies.