Journal Article
Reducing Subway Crowding: Analysis of an Off-peak Discount Experiment in Hong Kong

Increases in ridership are outpacing capacity expansions in a number of transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system, ;getting more out of their present capacity. This paper uses Hong Kong’s MTR system as a case study to explore the effects of crowding-reduction strategies and how to use fare data to support these measures. MTR introduced a discount in September 2014 to encourage users to travel before the peak and reduce on-board crowding. To understand the impacts of this intervention, first, existing congestion patterns were reviewed and a clustering analysis was used to reveal typical travel patterns among users. Then, changes to users’ departure times were studied at three levels to evaluate the promotion’s effects. Patterns among all users were measured across both the whole system and for specific rail segments. The travel patterns of the user groups, who have more homogeneous usage characteristics, were also evaluated, revealing groups to have differing responses to the promotion. The incentive was found to have impacted morning travel, particularly at the beginning of the peak hour and among users with commuter-like behavior. Aggregate and group-specific elasticities were developed to inform future promotions and the results were also used to suggest other potential incentive designs.

Title
Publication TypeJournal Article
Year of PublicationSubmitted
AuthorsHalvorsen A, Koutsopoulos HN, Lau S, Au T, Zhao J
Keywordsoff-peak discount, public transit, subway crowding, travel demand management
Abstract

Increases in ridership are outpacing capacity expansions in a number of transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system, ;getting more out of their present capacity. This paper uses Hong Kong’s MTR system as a case study to explore the effects of crowding-reduction strategies and how to use fare data to support these measures. MTR introduced a discount in September 2014 to encourage users to travel before the peak and reduce on-board crowding. To understand the impacts of this intervention, first, existing congestion patterns were reviewed and a clustering analysis was used to reveal typical travel patterns among users. Then, changes to users’ departure times were studied at three levels to evaluate the promotion’s effects. Patterns among all users were measured across both the whole system and for specific rail segments. The travel patterns of the user groups, who have more homogeneous usage characteristics, were also evaluated, revealing groups to have differing responses to the promotion. The incentive was found to have impacted morning travel, particularly at the beginning of the peak hour and among users with commuter-like behavior. Aggregate and group-specific elasticities were developed to inform future promotions and the results were also used to suggest other potential incentive designs.