Formulation and solution framework for real-time railway traffic management with demand prediction

Abstract

Recent transport policies increasingly promote shifts towards rail travel aiming at a more sustainable transportation system. This shift is hampered by widespread unexpected perturbations in operations, resulting in perceived poor punctuality and reliability. When prevention of such perturbations is not feasible, traffic management must mitigate their effects, resolving arising conflicts to restore regular train operations and minimize delay. Current practice generally includes the assessment of railway performance in terms of train delays, but the quality of service to passengers is rarely explicitly accounted for. A railway traffic management framework is proposed that accounts for both passenger and train delays. To do so, a predictive optimization framework is proposed, integrating a demand prediction module, a passenger demand assignment module and a traffic management module. The first dynamically predicts future origin-destination passenger flows using linear regression on real-time observed smart card data. Then, the demand assignment module links predicted passengers to specific train paths, given a railway schedule. Finally, the traffic management module optimizes train scheduling and routing in real time, under the combined objective of minimizing train and passenger delays. The methodology is validated and benchmarked against equivalent passenger agnostic traffic management on a case study of the Copenhagen suburban railway network. The results show that it is possible to take into account passenger perspective in railway traffic management, without reducing the railway system efficiency compared to classic approaches.