Integrated optimization of train stop planning and seat allocation for high-speed railways based on active management for dynamic demand

Proactive demand management methods can achieve a trade-off between supply and demand for high-speed railways. Focusing on multi-type demand configurations, four active demand management methods were designed, two single-scenario and two multi-scenario methods. An optimization model of stop planning and seat allocation was constructed to minimize the difference between demand and supply, number of trains, and train-stop costs. The number of train stops, transport capacity, and train occupancy rates were considered. Particle swarm optimization and CPLEX were combined to handle this significant linear programming problem. A real case study of different demand scenarios based on the Hohhot-Beijing high-speed railway in China verified the feasibility of active demand management methods. The results indicate that integrated optimization can be justified with mixed load patterns. Compared with the original scheme, the optimized scheme reduced the number of trains by 23.4% and increased train occupancy rate by more than 36.7%.