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Train Commuters' Scheduling Preferences: Evidence from a Large-Scale Peak Avoidance Experiment

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  • Stefanie Peer

    (VU University Amsterdam, the Netherlands, and Vienna University of Economics and Business, Vienna, Austria)

  • Jasper Knockaert

    (VU University Amsterdam, the Netherlands)

  • Erik Verhoef

    (VU University Amsterdam, the Netherlands)

Abstract

This discussion paper led to a publication in 'Transportation Science' . We study the trip scheduling preferences of train commuters in a real-life setting. The underlying data have been collected during large-scale peak avoidance experiment conducted in the Netherlands, in which participants could earn monetary rewards for traveling outside peak hours. The experiment included ca. 1000 participants and lasted for about 6 months. Holders of an annual train pass were invited to join the experiment, and a customized smartphone app was used to measure the travel behavior of the participants. We find that compared to the pre-measurement, the relative share of peak trips decreased by 22% during the reward period, and by 10% during the post-measurement. By combining multiple complementary data sources, we are able to specify and estimate (MNL and panel latent class) departure time choice models. These yield plausible estimates for the monetary values that participants attach to reducing travel time, schedule delays, the number of transfers, crowdedness, and unreliability.

Suggested Citation

  • Stefanie Peer & Jasper Knockaert & Erik Verhoef, 2015. "Train Commuters' Scheduling Preferences: Evidence from a Large-Scale Peak Avoidance Experiment," Tinbergen Institute Discussion Papers 15-078/VIII, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20150078
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    2. Vautard, Félix & Liu, Chengxi & Fröidh, Oskar & Byström, Camilla, 2021. "Estimation of interregional rail passengers’ valuations for their desired departure times," Transport Policy, Elsevier, vol. 103(C), pages 183-196.
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    4. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part I. Integrative synthesis of empirical evidence and conceptualisation of external validity," Papers 2102.02940, arXiv.org.
    5. Raphaël Homayoun Boroumand & Stéphane Goutte & Thomas Péran & Thomas Porcher, 2019. "Worker mobility and the purchase of low CO2 emission vehicles in France: a datamining approach," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 16(2), pages 171-205, December.
    6. Adnan, Muhammad & Nahmias Biran, Bat-hen & Baburajan, Vishnu & Basak, Kakali & Ben-Akiva, Moshe, 2020. "Examining impacts of time-based pricing strategies in public transportation: A study of Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 127-141.
    7. Yen, Barbara T.H. & Mulley, Corinne & Meza, Gerardo, 2023. "Exploring the attitudes and perceptions influencing user participation in gamification schemes for TDM," Research in Transportation Economics, Elsevier, vol. 99(C).
    8. Jinwon Kim & Jucheol Moon, 2022. "Congestion Costs and Scheduling Preferences of Car Commuters in California: Estimates Using Big Data," Working Papers 2201, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    9. Yang, Hai & Tang, Yili, 2018. "Managing rail transit peak-hour congestion with a fare-reward scheme," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 122-136.
    10. Jasper Knockaert & Stefanie Peer & Erik Verhoef, 2016. "Identification of self-selection biases in field experiments using stated preference experiments," Natural Field Experiments 00568, The Field Experiments Website.
    11. Anupriya, & Graham, Daniel J. & Hörcher, Daniel & Anderson, Richard J. & Bansal, Prateek, 2020. "Quantifying the ex-post causal impact of differential pricing on commuter trip scheduling in Hong Kong," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 16-34.
    12. Allister Loder & Fabienne Cantner & Lennart Adenaw & Markus Siewert & Sebastian Goerg & Markus Lienkamp & Klaus Bogenberger, 2022. "A nation-wide experiment: fuel tax cuts and almost free public transport for three months in Germany -- Report 1 Study design, recruiting and participation," Papers 2206.00396, arXiv.org.
    13. Raphaël Homayoun Boroumand & Stéphane Goutte & Thomas Péran & Thomas Porcher, 2019. "Worker mobility and the purchase of low CO2 emission vehicles in France: a datamining approach," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 16(2), pages 171-205, December.
    14. Jiang, Feng & Cacchiani, Valentina & Toth, Paolo, 2017. "Train timetabling by skip-stop planning in highly congested lines," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 149-174.
    15. An, Qinhe & Fu, Xiao & Huang, Di & Cheng, Qixiu & Liu, Zhiyuan, 2020. "Analysis of adding-runs strategy for peak-hour regular bus services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    16. Wang, Yu & Wang, Yacan & Ettema, Dick & Mao, Zidan & Charlton, Samuel G. & Zhou, Huiyu, 2020. "Commuter value perceptions in peak avoidance behavior: An empirical study in the Beijing subway system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 70-84.
    17. Peer, Stefanie & Börjesson, Maria, 2018. "Temporal framing of stated preference experiments: does it affect valuations?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 319-333.
    18. Liang, Jinpeng & Zang, Guangzhi & Liu, Haitao & Zheng, Jianfeng & Gao, Ziyou, 2023. "Reducing passenger waiting time in oversaturated metro lines with passenger flow control policy," Omega, Elsevier, vol. 117(C).

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    More about this item

    Keywords

    departure time decisions; scheduling; peak avoidance experiment; rail; trains; revealed preference data; smartphone app; GPS data; value of travel time; valuation of schedule delays; valuation of comfort; crowding costs; valuation of reliability;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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