Quantifying the ex-post causal impact of differential pricing on commuter trip scheduling in Hong Kong

Abstract

This paper quantifies the causal impact of differential pricing on the trip-scheduling of regular commuters using the Mass Transit Railway (MTR) in Hong Kong. It does so by applying a difference-in-difference (DID) method to large scale smart card data before and after the introduction of the Early Bird Discount (EBD) pricing intervention. We find statistically significant but small effects of the EBD in the form of earlier departure times. Leveraging the granularity of the data, we also allow for the treatment effect to vary over observed travel characteristics. Our empirical results suggest that fares and crowding are the key determinants of commuter responsiveness to the EBD policy.

Data-Driven Real-Time Denied Boarding Prediction in Urban Railway Systems

“…Similarly, San Francisco’s PERKS program resulted in a 10% shift from peak-time travel. Empirical studies reported factors affecting behavior change, such as flexibility, demographics, trip length, and discounts ( 18 , 19 ).…”

Impacts of the walking environment on mode and departure time shifts in response to travel time change: Case study in the multi-layered Hong Kong metropolis

“…Nevertheless, such high mode shares and good service levels still require extra soft and hard efforts from policy measures and infrastructure to integrate the services across all travel modes (Wang and Po, 2001;Luk and Olszewski, 2002;Zhe, 2017). Examples could be the off-peak travel discount, a transit demand management policy that targets trip scheduling by regular commuters, which aims at spreading peak loading (i.e., departure time shift) and achieving uniform diurnal ridership (Halvorsen et al, 2016(Halvorsen et al, , 2020Anupriya et al, 2020), and mode shift to rail policy such as the continuous expansion of the metro network with bus route rationalization Lo, 2008, 2010;Chan et al, 2021a). This study investigates the impact of walking environment on promoting mode and departure time shift intentions.…”

Social Distancing in Public Transport: Mobilising New Technologies for Demand Management under the COVID-19 Crisis

“…The downside of extensive reward schemes is the pressure it puts on the public budget at a time when transport operators are already severely financially constrained, and rewarding off-peak travellers might be unfair against those residents/voters who do not travel at all. While some empirical evidence on the success of rewarding does exist in the context of road use (Knockaert et al 2012), experience in public transport, as documented by Anupriya et al (2020a), suggests that the behavioural response might be slower, less intensive, and more expensive than what social distancing during the pandemic would require. On the theoretical front, promising new findings by Tang et al (2020a, b) indicate that the integration of fare-reward schemes with non-rewarding uniform fares may achieve demand management goals revenue neutrally.…”