Rescheduling and Rerouting of Connecting Trains after Perturbations

Overview & Synopsis

This thesis addresses the real-time Railway Traffic Management Problem (rtRTMP) in station control areas, an important issue in railway operations involving the adjustment of train timetables during perturbations to minimize delays and ensure smooth passenger transfers. Railway networks often face perturbations, leading to significant delays. Effective strategies are needed to mitigate these delays while maintaining the integrity of passenger connections.The primary objective of this research is to develop and evaluate enhancements to existing algorithms for the rtRTMP, focusing on reducing train delays and preserving passenger connections. To achieve this, we extend an existing Mixed-Integer Linear Programming (MILP) formulation for the rtRTMP by introducing two enhancements. Additionally, we extend an Ant Colony Optimization (ACO) algorithm for the Train Routing Selection Problem (TRSP), which reduces the number of alternative routes considered for trains, making rtRTMP instances more tractable. These enhancements aim to optimize train schedules by minimizing connection times during passenger transfers without compromising passenger connection integrity.We assess the impact of these proposed enhancements through extensive experimentation on multiple instances representing traffic conditions in the Lille-Flandres station control area in northern France. Results demonstrate significant improvements in reducing total train delays. The integration of these enhancements into both the TRSP and rtRTMP shows promising outcomes, indicating their potential for practical implementation in real-world railway traffic management systems.We incorporate RailLab, specialized software by Rail Concept for detailed infrastructure modeling and transport planning, into our research framework. Using RailLab, we create realistic instances to test the new infrastructure in our experiments and are in the process of testing our proposed enhancements. Additionally, we plan to translate our model's solutions into formats understandable by RailLab, aiding analysis and implementation.Overall, this research contributes to the advancement of railway traffic management strategies by offering innovative solutions to mitigate delay propagation and enhance passenger connection efficiency, thereby improving the reliability and performance of railway networks.