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

Focusing on dynamic demand for high-speed railways, demand management has its appeal in transforming its attention from supply-driven to on-demand. Active management involving with train stop planning and seat allocation is a key issue at the planning level. Concentrating on multi-type demand exhibitions, this paper designs four active demand management methods for different demand types, i.e., (i) a method determined by the mean value, (ii) a method based on the probability of passenger flow in a time period, (iii) a multi-scenario method based on each day, and (iv) a method as per similar passenger flow combinations, respectively. An integrated optimization model of stop planning, and seat allocation is constructed with the objective of minimizing the difference between demand and supply, the number of the trains and the cost of train stops. The number of train stops, carrying capacity, user satisfaction degree, and the seat attendance rate are taken into consideration. An algorithm combined particle swarm with CPLEX is proposed to handle this large-scale linear programming problem. A real case of different demand scenarios based on the Hohhot-Beijing high-speed railway in China verifies that the active demand management methods are feasible for different demand-type scenarios. The results indicate that the integrated optimization can be justified in many cases with mixed load patterns. Compared with the original scheme, the optimized train stop planning reduces the total number of trains by 23.2% and increases the seat attendance rate by 33.8%, respectively.