Optimization of High-Speed Railway Operation Schedule Based on Passenger Flow Imbalance Characteristics

With the continuous expansion and network formation of China’s high-speed railway (HSR) network, the unbalanced temporal and spatial distribution of passenger flow has become a prominent problem restricting the refined operation and high-efficiency management of HSR systems. Long-term peak-trough passenger flow differences, unbalanced passenger volume in uplink and downlink directions, and mismatches between train capacity and passenger demand lead to frequent idle carriage resources, overload operation in key sections, and low overall operation benefit. To solve the above operational management pain points, this paper takes the trunk lines of China’s domestic high-speed railway network as the research object, based on authentic passenger flow and timetable dataset collected from China State Railway Group operation big data platform, covering 4 core HSR trunk lines (Beijing-Shanghai, Beijing-Guangzhou, Shanghai-Wuhan, Guangzhou-Shenzhen) from January 2022 to March 2025; raw data adopts 15-min time granularity statistical samples, total effective sample volume reaches 286,420 groups, abnormal outlier data caused by temporary line maintenance and sudden natural disasters is eliminated via 3σ criterion in data preprocessing actual passenger flow monitoring data and train operation schedule data from 2022 to 2025. By analyzing the temporal-spatial imbalance characteristics of HSR passenger flow, a multi-objective train operation schedule optimization model is constructed with the goals of minimizing passenger waiting time, maximizing train load factor, and minimizing enterprise operation cost. The three optimization objectives are selected following mainstream macroscopic timetable optimization research paradigm, passenger waiting time minimization represents passenger service benefit, load factor maximization reflects transport resource utilization benefit, operation cost minimization corresponds to railway enterprise economic benefit; entropy weight method is adopted to realize multi-objective weighted aggregation referring to existing multi-objective timetable research achievements, the final obtained optimization result is the single optimal compromise solution calculated after objective weighting rather than alternative solutions selected from Pareto optimal set. Combined with line capacity constraints, train marshaling specifications, and peak hour operation rules, an improved adaptive genetic algorithm is designed for model solving. Detailed implementation contents of the improved adaptive genetic algorithm, including chromosome coding,