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Congestion Costs and Scheduling Preferences of Car Commuters in California: Estimates Using Big Data

Author

Listed:
  • Jinwon Kim

    (Department of Economics, Sogang University, Seoul, Korea)

  • Jucheol Moon

    (Department of Computer Engineering & Computer Science, California State University, Long Beach)

Abstract

This paper aims to quantify congestion costs and estimate the scheduling utility function for commuters. To do so, we construct California commuters' travel-time profiles, namely, the menu of travel times that each individual will likely face according to alternate trip timing choices. On average, California commuters waste about 5 minutes per morning commute due to congestion. Commuters facing a higher congestion level at the peak hour tend to avoid congestion delays by arriving at an inconvenient edge time. We also discover that for the majority of the commuters in our data, travel-time profiles are much flatter than our estimated schedule utility. From this finding, we question the accuracy of the existing bottleneck models in quantifying the economic costs of congestion and the optimal toll to ameliorate congestion.

Suggested Citation

  • 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).
    • Handle: RePEc:sgo:wpaper:2201
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    References listed on IDEAS

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    1. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    2. Alex Anas, 2015. "Why Are Urban Travel Times So Stable?," Journal of Regional Science, Wiley Blackwell, vol. 55(2), pages 230-261, March.
    3. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    4. Verhoef, Erik T., 2020. "Optimal congestion pricing with diverging long-run and short-run scheduling preferences," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 191-209.
    5. Arnott, Richard & de Palma, Andre & Lindsey, Robin, 1993. "A Structural Model of Peak-Period Congestion: A Traffic Bottleneck with Elastic Demand," American Economic Review, American Economic Association, vol. 83(1), pages 161-179, March.
    6. Peer, Stefanie & Knockaert, Jasper & Verhoef, Erik T., 2016. "Train commuters’ scheduling preferences: Evidence from a large-scale peak avoidance experiment," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 314-333.
    7. Fosgerau, Mogens & de Palma, André, 2012. "Congestion in a city with a central bottleneck," Journal of Urban Economics, Elsevier, vol. 71(3), pages 269-277.
    8. Kenneth A. Small & Clifford Winston & Jia Yan, 2005. "Uncovering the Distribution of Motorists' Preferences for Travel Time and Reliability," Econometrica, Econometric Society, vol. 73(4), pages 1367-1382, July.
    9. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-260, May.
    10. Fosgerau, Mogens & Kim, Jinwon, 2019. "Commuting and land use in a city with bottlenecks: Theory and evidence," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 182-204.
    11. Jonathan D Hall, 2021. "Can Tolling Help Everyone? Estimating the Aggregate and Distributional Consequences of Congestion Pricing," Journal of the European Economic Association, European Economic Association, vol. 19(1), pages 441-474.
    12. Akbar, Prottoy & Duranton, Gilles, 2017. "Measuring the Cost of Congestion in Highly Congested City: Bogotá," Research Department working papers 1028, CAF Development Bank Of Latinamerica.
    13. Stefanie Peer & Erik Verhoef & Jasper Knockaert & Paul Koster & Yin‐Yen Tseng, 2015. "Long‐Run Versus Short‐Run Perspectives On Consumer Scheduling: Evidence From A Revealed‐Preference Experiment Among Peak‐Hour Road Commuters," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 303-323, February.
    14. Arnott, Richard & de Palma, Andre & Lindsey, Robin, 1990. "Economics of a bottleneck," Journal of Urban Economics, Elsevier, vol. 27(1), pages 111-130, January.
    15. Small, Kenneth A., 2012. "Valuation of travel time," Economics of Transportation, Elsevier, vol. 1(1), pages 2-14.
    16. Tarduno, Matthew, 2021. "The congestion costs of Uber and Lyft," Journal of Urban Economics, Elsevier, vol. 122(C).
    17. Stefanie Peer & Erik Verhoef & Jasper Knockaert & Paul Koster & Yin‐Yen Tseng, 2015. "Long‐Run Versus Short‐Run Perspectives On Consumer Scheduling: Evidence From A Revealed‐Preference Experiment Among Peak‐Hour Road Commuters," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(1), pages 303-323, February.
    18. Gabriel E. Kreindler & Yuhei Miyauchi, 2019. "Measuring Commuting and Economic Activity inside Cities with Cell Phone Records," Boston University - Department of Economics - Working Papers Series WP2020-006, Boston University - Department of Economics, revised Apr 2020.
    19. Tang, Cheng Keat, 2021. "The Cost of Traffic: Evidence from the London Congestion Charge," Journal of Urban Economics, Elsevier, vol. 121(C).
    20. Hjorth, Katrine & Börjesson, Maria & Engelson, Leonid & Fosgerau, Mogens, 2015. "Estimating exponential scheduling preferences," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 230-251.
    21. Fosgerau, Mogens & Kim, Jinwon & Ranjan, Abhishek, 2018. "Vickrey meets Alonso: Commute scheduling and congestion in a monocentric city," Journal of Urban Economics, Elsevier, vol. 105(C), pages 40-53.
    22. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-479, June.
    23. Hörcher, Daniel & Graham, Daniel J. & Anderson, Richard J., 2017. "Crowding cost estimation with large scale smart card and vehicle location data," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 105-125.
    24. Kim, Jinwon, 2019. "Estimating the social cost of congestion using the bottleneck model," Economics of Transportation, Elsevier, vol. 19(C), pages 1-1.
    25. Jun Yang & Avralt-Od Purevjav & Shanjun Li, 2020. "The Marginal Cost of Traffic Congestion and Road Pricing: Evidence from a Natural Experiment in Beijing," American Economic Journal: Economic Policy, American Economic Association, vol. 12(1), pages 418-453, February.
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    Cited by:

    1. Jinwon Kim & Dede Long, 2022. "What Flattened the House-Price Gradient? The Role of Work-from-Home and Decreased Commuting Cost," Working Papers 2205, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).

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    More about this item

    Keywords

    congestion costs; scheduling preference; commuting; Google Maps; big data; machine learning;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • H21 - Public Economics - - Taxation, Subsidies, and Revenue - - - Efficiency; Optimal Taxation

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