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

Jinwon Kim and Jucheol Moon ()
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Jucheol Moon: Department of Computer Engineering & Computer Science, California State University, Long Beach

No 2201, Working Papers from Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy)

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.

Keywords: congestion costs; scheduling preference; commuting; Google Maps; big data; machine learning (search for similar items in EconPapers)
JEL-codes: C25 C8 H21 R41 R48 (search for similar items in EconPapers)
Pages: 54 pages
Date: 2022
New Economics Papers: this item is included in nep-big, nep-tre, nep-upt and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sgo:wpaper:2201

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