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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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    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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