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.

[1]  Harry Timmermans,et al.  Understanding travellers’ preferences for different types of trip destination based on mobile internet usage data , 2018 .

[2]  Amy N. Finkelstein.  The effect of tax subsidies to employer-provided supplementary health insurance: evidence from Canada , 2002 .

[3]  Jeffrey M. Wooldridge,et al.  Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data , 2003 .

[4]  Anne Halvorsen,et al.  Improving transit demand management with Smart Card data : general framework and applications , 2015 .

[5]  Arnab Majumdar,et al.  The effects of congestion charging on road traffic casualties: a causal analysis using difference-in-difference estimation. , 2012, Accident; analysis and prevention.

[6]  Haris N. Koutsopoulos,et al.  Optimal design of promotion based demand management strategies in urban rail systems , 2019 .

[7]  Richard J. Anderson,et al.  A Dynamic Panel Analysis of Urban Metro Demand , 2009 .

[8]  Haris N. Koutsopoulos,et al.  Reducing Subway Crowding: Analysis of an Off-Peak Discount Experiment in Hong Kong , 2016 .

[9]  Erik T. Verhoef,et al.  Train Commuters' Scheduling Preferences: Evidence from a Large-Scale Peak Avoidance Experiment , 2015 .

[10]  Graham Currie,et al.  Quick and Effective Solution to Rail Overcrowding: Free Early Bird Ticket Experience in Melbourne, Australia , 2010 .

[11]  Gerard Whelan,et al.  Modelling the impact of alternative fare structures on train overcrowding , 2004 .

[12]  André de Palma,et al.  Discomfort in Mass Transit and its Implication for Scheduling and Pricing , 2013 .

[13]  Daniel J. Graham,et al.  Crowding cost estimation with large scale smart card and vehicle location data , 2017 .

[14]  A. Palma,et al.  The economics of crowding in rail transit , 2017 .

[15]  Derek D. Reed,et al.  Personalized incentives for promoting sustainable travel behaviors , 2020 .

[16]  J. Rouwendal,et al.  Does improving public transport decrease car ownership? Evidence from a residential sorting model for the Copenhagen metropolitan area , 2020, Regional Science and Urban Economics.

[17]  Eran Ben-Elia,et al.  Changing commuters' behavior using rewards: a study of rush-hour avoidance , 2011 .

[18]  S. Redding,et al.  The Making of the Modern Metropolis: Evidence from London* , 2020 .

[19]  Haris N. Koutsopoulos,et al.  Behavioral Response to Transit Demand Management Promotions: Sustainability and Implications for Optimal Promotion Design , 2019 .

[20]  Alberto Abadie.  Semiparametric Difference-in-Differences Estimators , 2005 .

[21]  J. Preston,et al.  The demand for public transport: The effects of fares, quality of service, income and car ownership , 2006 .

[22]  Peng Zhao,et al.  Measuring Retiming Responses of Passengers to a Prepeak Discount Fare by Tracing Smart Card Data: A Practical Experiment in the Beijing Subway , 2019, Journal of Advanced Transportation.

[23]  D. Ettema,et al.  Rewarding rush-hour avoidance: A study of commuters' travel behavior. , 2011 .

[24]  Hai-Jun Huang,et al.  Fares and tolls in a competitive system with transit and highway: the case with two groups of commuters , 2000 .

[25]  Kenneth A. Small,et al.  THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS , 1982 .

[26]  S. Managi,et al.  How does commuting behavior change due to incentives? An empirical study of the Beijing Subway System , 2014, Transportation Research Part F: Traffic Psychology and Behaviour.

[27]  Orley Ashenfelter,et al.  Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs , 1984 .

[28]  Jeremy Shires,et al.  Impact of Delays on Passenger Train Services , 2009 .

[29]  Yuichiro Yoshida,et al.  The Commuter's Time-of-Use Decision and Optimal Pricing and Service in Urban Mass Transit , 2002 .

[30]  Yiqing Xu,et al.  How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice , 2016, Political Analysis.

[31]  Peter White,et al.  The Potential of Public Transport Smart Card Data , 2005 .

[32]  D. Ettema,et al.  Carrots versus sticks: Rewarding commuters for avoiding the rush-hour— a study of willingness to participate , 2009 .

[33]  Bruno Agard,et al.  MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA , 2006 .

[34]  Jean-Claude Thill,et al.  Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing , 2013, Comput. Environ. Urban Syst..

[35]  M. Wardman.  Price elasticities of surface travel demand: a meta-analysis of UK evidence , 2014 .

[36]  Keith Sloan,et al.  A review of policy and economic instruments for peak demand management in commuter rail , 2010 .