Bingfeng Si

Bilevel Programming for Evaluating Revenue Strategy of Railway Passenger Transport Under Multimodal Market Competition

A bilevel programming approach is used to study the strategies of increasing revenue of the railway agency running between Beijing and Tianjin, China. Bilevel programming approaches have been used in many studies to tackle a variety of transportation problems, but rarely for railway revenue strategy analysis. In this paper, the upper-level problem of the bilevel programming is to determine optimal pricing, speed, and level-of-service (LOS) strategy that maximizes the revenue of the railway agency. The lower-level problem describes passengers’ mode choice behavior under a transportation market with three competing modes: bus, rail, and car. The lower-level problem is to minimize the travelers cost in terms of money, time, and other related factors such as comfort and safety. A generalized cost function, considering these factors together with a logit model, is used to simulate travelers’ mode choice behavior. Study results clearly show that the bilevel programming is appropriate for the problem studied here. The results indicate that, to increase its revenue, the railway agency should focus on not only the pricing but also travel time and LOS. A pricing breakpoint of about ¥31 (¥7 = U¥1) is found, as it results in the highest revenue for all traveling speeds and LOS. Further increase of the price leads to reduced revenue. A consistent revenue increase trend is observed for all higher traveling speeds and LOS, which emphasizes that the railway agency should pay attention to a combined revenue strategy.

Data-Based Sorting Algorithm for Variable Message Sign Location: Case Study of Beijing

Variable message signs (VMSs) provide important traffic information to help drivers travel better on a transportation network. The effectiveness of VMSs largely depends on the numbers and locations of VMSs in a transportation network. Although a few optimization models have been proposed to find candidate roads for locating VMSs, few have been devoted to developing algorithms that can be used in a real transportation network. A large amount of traffic data, such as traffic flow data, is widely available, collected by various means. Based on those traffic data, a sorting algorithm for a VMS location problem is presented in this paper. The algorithm gave a VMS location order rather than a location set. The proposed method divided roads into categories according to multilevel attributes and preferentially selected roads of a higher class with larger flows and more information and the minimal effect of existing VMS in a certain order to locate VMS. The proposed algorithm was analyzed and verified through a practical case on the Beijing, China, urban road network.