Dynamic Scheduling of Trains in Densely Populated Congested Areas

2 Disclaimer The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Transportation assume no liability for the contents or use thereof. The contents do not necessarily reflect the official views or policies of the State of California or the Department of Transportation. This report does not constitute a standard, specification, or regulation. 3 Abstract In the U.S., freight railways are one of the major means to transport goods from ports to inland destinations. According to the Association of American Railroad's study, rail companies move more than 40% of the nation's total freight. Given the fact that the freight railway industry is already running without much excess capacity, better planning and scheduling tools are needed to effectively manage the scarce resources, in order to cope with the rapidly increasing demand for railway transportation. Freight train scheduling and dispatching is one important aspect of freight railroad management. This research tries to develop algorithms for static and dynamic scheduling of freight trains. Two optimization based algorithms are first proposed to solve the static train scheduling problem for small rail networks. The proposed LtdFlePath and GA+FixedPath algorithms are able to outperform two existing heuristics, PureGA and Greedy algorithm, in terms of railway total train delay. And the CPU solution times of the proposed heuristics are within a reasonable time constraint. Then two decomposition based heuristics, the Decomp and Parallel algorithm, are developed to solve the train scheduling problems for larger networks. Both algorithms significantly outperform existing algorithms. Finally, we move to dynamic scheduling of trains. We present one heuristic which solves the dynamic scheduling problem for both small and large networks. This Dynamic algorithm is able to reduce delay by at least 40% of existing algorithms on representative rail scenarios.