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Combining GPS Tracking and Surveys for a Mode Choice Model: Processing Data from a Quasi-Natural Experiment in Germany

Author

Listed:
  • Heike Link
  • Dennis Gaus
  • Neil Murray
  • Maria Fernanda Guajardo Ortega
  • Flavien Gervois
  • Frederik von Waldow
  • Sofia Eigner

Abstract

This paper deals with the data generation process implemented for an analysis of the impact of the 9-Euro ticket on mode choice. We discuss the assumptions made and procedures used to process a raw dataset that is based on GPS traces of individuals’ movements and on survey data into the choice-set for a discrete choice model. Several steps of cleaning and merging are described in order to a) obtain a reliable dataset; b) define available modal alternatives with attributes such as distance, duration, and costs; and c) impute the travel purpose for each movement to form. Our main contribution is to show that a systematic analysis of the sample obtained at different stages of data processing is important to make sure that the final sample is unbiased. Furthermore, we contribute by analysing the difference between observed travel time and travel time calculated by routing tools such as Google Maps. We show that the often- employed approach of estimating RP based choice models on the basis of observed travel times for the chosen mode of transport but calculated travel times for the non-chosen alternatives can introduce a structural bias into the sample.

Suggested Citation

  • Heike Link & Dennis Gaus & Neil Murray & Maria Fernanda Guajardo Ortega & Flavien Gervois & Frederik von Waldow & Sofia Eigner, 2023. "Combining GPS Tracking and Surveys for a Mode Choice Model: Processing Data from a Quasi-Natural Experiment in Germany," Discussion Papers of DIW Berlin 2047, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp2047
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    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.878044.de/dp2047.pdf
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    References listed on IDEAS

    as
    1. de Grange, Louis & González, Felipe & Muñoz, Juan Carlos & Troncoso, Rodrigo, 2013. "Aggregate estimation of the price elasticity of demand for public transport in integrated fare systems: The case of Transantiago," Transport Policy, Elsevier, vol. 29(C), pages 178-185.
    2. Calastri, Chiara & Hess, Stephane & Choudhury, Charisma & Daly, Andrew & Gabrielli, Lorenzo, 2019. "Mode choice with latent availability and consideration: Theory and a case study," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 374-385.
    3. Tsoleridis, Panagiotis & Choudhury, Charisma F. & Hess, Stephane, 2022. "Deriving transport appraisal values from emerging revealed preference data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 225-245.
    4. Link, Heike, 2015. "Is car drivers’ response to congestion charging schemes based on the correct perception of price signals?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 96-109.
    5. Martínez, Francisco & Aguila, Felipe & Hurtubia, Ricardo, 2009. "The constrained multinomial logit: A semi-compensatory choice model," Transportation Research Part B: Methodological, Elsevier, vol. 43(3), pages 365-377, March.
    6. Peer, Stefanie & Knockaert, Jasper & Koster, Paul & Verhoef, Erik T., 2014. "Over-reporting vs. overreacting: Commuters’ perceptions of travel times," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 476-494.
    7. Prateek Bansal & Daniel Hörcher & Daniel J. Graham, 2022. "A dynamic choice model to estimate the user cost of crowding with large‐scale transit data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 615-639, April.
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    Cited by:

    1. Maria Fernanda Guajardo Ortega & Heike Link, 2023. "Estimating Mode Choice Inertia and Price Elasticities after a Price Intervention – Evidence from Three Months of almost Fare-free Public Transport in Germany," Discussion Papers of DIW Berlin 2052, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    Data processing; travel behaviour; GPS traces; discrete choice models; revealed preferences;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • 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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