Abstract

Given a railway line with n stops and the number of travelers between each pair of stops, we show how to split these stops into k different fare zones in order to maximize the benefit obtained from the sale of tickets to the travelers. We present a method to obtain this solution that is based on finding the longest path in a weighted root tree. This method improves in terms of efficiency the combinatorial method, where all the possible distributions have to be considered for deciding which is the optimal one. The authors would like to thank RENFE for his collaboration and providing us several tables of data of the flow of passengers of the commuter trains of the Valencia region. J. Alberto Conejero is supported by MEC Project MTM2013-47093-P and Esther Sanabria-Codesal is supported by MEC Project MTM2012-33073. Conejero Casares, JA.; Jordan Lluch, C.; Sanabria Codesal, E. (2014). A tree-based model for setting optimal train fare zones. Mathematical Problems in Engineering. 2014:1-11. doi:10.1155/2014/384321 S 1 11 2014 Document type: Article

Abstract

High-speed railway (HSR) is recognized as a green transportation mode with lower energy consumption and less pollution emission than other transportation. At present, China has the largest HSR network globally, but the maximum revenue of railway transportation corporations has not been realized. In order to make HSR achieve a favorable position within the fierce competition in the market, increase corporate revenue, and achieve the sustainable development of HSR and railway corporations, we introduce the concept of revenue management in HSR operations and propose an innovative model to optimize the price and seat allocation for HSR simultaneously. In the study, we formulate the optimization problem as a mixed-integer nonlinear programming (MINLP) model, which appropriately captures passengers&rsquo choice behavior. To reduce the computational complexity, we further transform the proposed MINLP model into an equivalent model. Finally, the effectiveness of both the proposed model and solution algorithm are tested and validated by numerical experiments. The research results show that the model can flexibly adjust the price and seat allocation of the corresponding ticketing period according to the passenger demand, and increase the total expected revenue by 5.92% without increasing the capacity. Document type: Article