Fare incentive strategies for managing peak-hour congestion in urban rail transit networks

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.