Fare incentive strategies for managing peak-hour congestion in urban rail transit networks
Document Type
Journal Article
Publication Date
2022
Subject Area
place - urban, place - asia, mode - rail, mode - subway/metro, planning - surveys, ridership - behaviour, policy - fares, policy - congestion
Keywords
Urban rail transit, departure choice, discrete choice model, fare incentive strategy
Abstract
In urban rail transit (URT) systems, fare incentives are emerging as a method to manage peak hour congestion. In this study, we propose a practical framework to model the departure time and route choice of URT passengers during peak hours. First, various attributes that influence the departure choice of passengers are investigated, and the willingness of passengers to shift their departure time or routes is evaluated based on a questionnaire survey of passengers of the Shanghai metro. Then, a discrete choice model is used to identify the interrelationships between fare incentives and the choice behaviors of passengers. We propose the following two fare incentive strategies: a time-based fare incentive strategy (TBFIS); and a route-based fare incentive strategy (RBFIS). These strategies consider changes in both time and space. Finally, the effectiveness of the two different fare incentive strategies is evaluated using the Shanghai URT network simulation system.
Rights
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Recommended Citation
Zhou, F., Li, C., Huang, Z., Xu, R., & Fan, W. (2022). Fare incentive strategies for managing peak-hour congestion in urban rail transit networks. Transportmetrica A: Transport Science, Vol. 18(1), pp. 166-187.
