An Integrated Pricing/Dispatching Strategy To Optimize Passenger Rail Operation Planning

Faced with the competition from road and air transport, high-speed railway dynamic pricing has been fully liberalized. Railway Company has won the right of "High-speed Rail Pricing" during its market transition phase that it can make their own pricing strategy. Railway Company can implement discounted fares, according to competitive market conditions, traffic distribution, and so on. The high-speed rail pricing independently can show that pricing mechanism reform is urgently needed. Railway Company need introduce scientific management methods to enhance the core competitiveness and increase passenger revenue, under the guide of passengers’actual needs.A limitation of the state-of-the-art in this area of research is that supply-side and demand-side issues are generally considered separately. The contribution of this paper is to propose a revenue-maximization model that integrates these two dimensions, based on dynamic ticket-pricing, elasticity in passenger demand, and flexible dispatching. The model considers both operating costs directly borne by the rail service operator and costs indirectly incurred by passengers due to the disutility of unproductive travel time (i.e. the value of time). The strategy to identify the profit-maximizing joint pricing/dispatching strategy is based on Lyapunov optimization. The drift-plus-penalty with perturbation technique is employed to solve the linear programing optimization problem. The Guangzhou-Shenzhen railway (Guangdong, PR China) is employed as the empirical case study, to expose the properties of the proposed model.The core content of this paper as follows:1. A multi-link and single-stage fare dynamic pricing model for operation planning is made. Passenger transport rate, utility functions, passenger transport model, pricing and cost model are included to maximize the revenue.2. The original train decision model was turned into a distributed algorithm by Lyapunov drift-plus-penalty, which greatly simplifies the calculation process. By optimizing the value of V, the utility and revenue can reach a balanced state. With V increasing, the objective function can gradually converge.3. We use Guangzhou-Shenzhen high-speed rail line as an example to comparative analysis the revenue of the fixed ticket-pricing regime, the optimized fixed-price pricing strategy, a fixed percentage of the price and dynamic pricing in working days and weekends.