Joint Optimization of Ticket Pricing Strategy and Train Stop Plan for High-Speed Railway: A Case Study

Qin, Jin; Xi-qiong, LI; Yang, Kang; Xu, Guangming

doi:10.3390/math10101679

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Yu et al [24] proposed a data-driven ticket dynamic pricing methodology for a railway service provider. Qin et al [25] considered that passenger demand is sensitive to the generalized travel cost and that the train's stopping plan can affect the travel time and passenger distribution. Then, a mixed-integer non-linear optimization model was proposed for the joint problem of ticket pricing and trains' stop planning to maximize the HSR's transport revenue and minimize passengers' travel time.…”

Section: Dynamic-pricing Problemmentioning

confidence: 99%

“…(2) Difference and innovation From the perspective of the application of revenue management theory, ticket pricing and ticket allocation are usually the key processes and decisions enacted by railway transportation enterprises to maximize their revenue [29]. In the previous studies, some researchers solved the problem of ticket allocation under a fixed ticket price [5,8], some solved the problem of dynamic pricing under a fixed ticket pre-allocation [17,25], and only a few studies explored the joint optimization problem of ticket pricing and allocation [30,33]. We think that ticket pricing and allocation are inseparable from each other for high-speed railway passenger transport revenue management decisions.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

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Joint Optimization of Ticket Pricing and Allocation on High-Speed Railway Based on Dynamic Passenger Demand during Pre-Sale Period: A Case Study of Beijing–Shanghai HSR

et al. 2022

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Against the background of the gradual deepening of China Railway’s market-oriented reform, and in order to improve the revenue and competitiveness for high-speed railway (HSR) passenger transport, this paper studies the joint optimization problem of the high-speed railway ticket pricing and allocation considering the dynamic demand characteristics of passengers during the pre-sale period. Firstly, we use the compound non-homogeneous Poisson process to describe the passengers' ticket-purchasing process and use the sparse method to simulate the passengers' ticket demand during the pre-sale period. Secondly, taking the ticket pricing and allocation as the decision variables and considering the full utilization of the train seat capacity, a stochastic nonlinear-programming mathematical model is established with the goal of maximizing the train revenue. A particle swarm algorithm is designed to solve the model. Finally, this study takes the G19 train running on the Beijing–Shanghai HSR in China as a case study to verify the effectiveness of the model and algorithm. The results show that the joint optimization scheme of ticket pricing and allocation considering dynamic demand yields a revenue of CNY 601,881, which increases the revenue by 1.01% with a small adjustment of the price compared with the fixed ticket price and pre-allocation scheme. This study provides scientific support for the decisions made by railway transportation enterprises, which is conducive to further increasing the potential ticket revenue and promoting sustainable development.

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Section: Dynamic-pricing Problemmentioning

confidence: 99%

Section: Comparison With Previous Studiesmentioning

confidence: 99%

Joint Optimization of Ticket Pricing and Allocation on High-Speed Railway Based on Dynamic Passenger Demand during Pre-Sale Period: A Case Study of Beijing–Shanghai HSR

et al. 2022

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Machine Learning Models for Predicting Movie Ticket Pricing

Obagbuwa,

Mwanza

2024

2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)

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Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes

et al. 2023

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To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system.

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Joint Optimization of Ticket Pricing Strategy and Train Stop Plan for High-Speed Railway: A Case Study

Cited by 5 publications

References 28 publications

Joint Optimization of Ticket Pricing and Allocation on High-Speed Railway Based on Dynamic Passenger Demand during Pre-Sale Period: A Case Study of Beijing–Shanghai HSR

Joint Optimization of Ticket Pricing and Allocation on High-Speed Railway Based on Dynamic Passenger Demand during Pre-Sale Period: A Case Study of Beijing–Shanghai HSR

Machine Learning Models for Predicting Movie Ticket Pricing

Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes

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