关键词: 铁路运输, 旅客到发时段匹配, 遗传算法, 列车开行方案, 乘降方便度

Abstract: In existing theoretical research, the high-speed railway train operational plans generally rely on time-varying passenger demand, facing challenges such as difficulty in obtaining accurate and comprehensive data, and disconnection between theory and reality. By contrast, the data based on the passenger arrival and departure time windows is simpler and more available in optimizing train operational plans. This paper introduces the convenience levels of passenger boarding and alighting at both departure and arrival ends during different time periods throughout the day as the indicator to measure arrival-departure time windows. The travel time of trains, including delays due to conflicts, is considered as the operational cost for enterprises. The passenger travel cost is determined based on the impact of train stops and the convenience level of passenger boarding and alighting, and the objective function is built by integrating these two costs. The constraints such as train candidate set and train coverage during convenient time periods are also included in the model. A genetic algorithm is used and the passenger flow distribution strategies are designed in conjunction with railway ticket selling practice. Multi- neighborhood genetic operators for train operation plans are designed based on train operation periods, departure time and stops. An adaptive mechanism for genetic operations is introduced to enhance the search efficiency of neighborhood solutions. A case example is analyzed for the Beijing-Shanghai High-speed Railway to verify the effectiveness of the model and algorithm. The result shows that the average passenger occupancy rate of the line is 69.4% and the train operation structure, time period distribution and capacity utilization are reasonable. Compared to the actual train operation plan, the total convenience level of passengers in the optimized plan increases by approximately 18.2% and the number of Origin to Destination (OD) pairs with high convenience level significantly increases, resulting in more balanced passenger travel experience and significant improvement in train operation efficiency. The result indicates that train operation plans optimization can be solved effectively by the passenger arrival and departure time windows and the proposed optimization method provides practical references for railway passenger transport departments in making decisions on train operation plans.

Key words: railway transportation, passenger arrival and departure time matching, genetic algorithm, train operation plan, the convenience of boarding and alighting