Reducing Subway Crowding: Analysis of an Off-Peak Discount Experiment in Hong Kong

Abstract

Increases in ridership are outpacing capacity expansions in several transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system and get more out of their present capacity. This paper uses Hong Kong’s Mass Transit Railway (MTR) system as a case study to explore the effects of crowding reduction strategies and how to use fare data to support these measures. The MTR system introduced a discount in September 2014 to encourage users to travel before the peak and reduce onboard 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 of 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 and revealed groups who had differing responses to the promotion. The incentive was found to have affected morning travel, particularly at the beginning of the peak hour period 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.

Do commuters adapt to in-vehicle crowding on trains?

“…Their study investigated travelers’ route choice in public transport using automated fare collection data on travelers, including smart card data. Despite the attempt to simulate reality, there are a limited number of RP studies that have estimated in-vehicle crowding costs (Halvorsen et al 2016 ; Tirachini et al 2016 ). While the RP studies are superior in terms of controlling the real variability of in-vehicle conditions during a trip, the SP method still has the advantage of collecting target variables more easily even if the variable is unobservable in reality.…”

The economics of crowding in rail transit

“…In 2014, Hong Kong's Mass Transit Railway system introduced an Early Bird Discount Promotion to encourage users to travel before the peak. The effects are described in Halvorsen et al (2016).…”

Identifying Urban Functional Areas and Their Dynamic Changes in Beijing: Using Multiyear Transit Smart Card Data

“…Furthermore, classifying passengers into different clusters derived from their travel patterns can infer their socio-economic attributes (Goulet-Langlois., 2016;Zhu et al, 2018), and enable analysis of potential factors which may affect passengers' travel elasticity (e.g. avoid travelling at peak times) (Halvorsen et al, 2016;Huang et al, 2019).…”