Optimizing resource allocation for delay control in high-speed rail networks

Train delays are a common issue in high-speed rail (HSR) networks. If not addressed promptly, they can propagate across multiple stations, reducing operational efficiency, negatively impacting passenger experience and causing economic losses. In this paper, we investigate strategies to mitigate the widespread propagation of delays in HSR networks from the perspective of resource allocation. First, we model delay propagation as a Normal-Delayed-Normal (NDN) spreading process, incorporating both self-induced delays at a station and delays caused by neighboring stations. We then formulate an optimization problem that seeks to allocate limited resources to stations to effectively control delays. To solve this problem, we propose heuristic resource allocation strategies based on network centrality measures, such as node degree and node betweenness. Furthermore, we develop a simulated annealing algorithm to find the optimal resource allocation solution. Experimental results based on data from a real-world HSR network show that degree-based strategies outperform other heuristic methods, while simulated annealing achieves the best overall performance, significantly enhancing the resilience of the HSR system against delay propagation.