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Occurence, frequency, and duration of commuters' work-to-home departure delay

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  • Mannering, Fred L.
  • Hamed, Mohammad M.

Abstract

This paper investigates commuters' decision to delay their departure from work to home, in an effort to avoid traffic congestion. A sample of commuters, from the congested Seattle metropolitan area, is used to estimate a model of the decision to delay homeward departure as well as models of the frequency and duration of this delay. The estimation results suggest, as expected, that traffic system characteristics dominate the delay choice, with socioeconomic characteristics and the characteristics of the area near the work location (which provides activity opportunities that can be undertaken during the departure delay) playing a lesser role. The estimated magnitude of influence that these determinants have, on the delay choice, has important implications for future departure-time-choice research.

Suggested Citation

  • Mannering, Fred L. & Hamed, Mohammad M., 1990. "Occurence, frequency, and duration of commuters' work-to-home departure delay," Transportation Research Part B: Methodological, Elsevier, vol. 24(2), pages 99-109, April.
    • Handle: RePEc:eee:transb:v:24:y:1990:i:2:p:99-109
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    Cited by:

    1. Bhat, Chandra R. & Sardesai, Rupali, 2006. "The impact of stop-making and travel time reliability on commute mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 709-730, November.
    2. Bhat, Chandra R. & Steed, Jennifer L., 2002. "A continuous-time model of departure time choice for urban shopping trips," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 207-224, March.
    3. Bhat, Chandra R., 2022. "A new closed-form two-stage budgeting-based multiple discrete-continuous model," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 162-192.
    4. Agarwal, Sumit & Diao, Mi & Keppo, Jussi & Sing, Tien Foo, 2020. "Preferences of public transit commuters: Evidence from smart card data in Singapore," Journal of Urban Economics, Elsevier, vol. 120(C).
    5. Lesley Strawderman & Daniel W. Carruth & Kathleen Sherman-Morris & Philip Menard & Merrill Warkentin & Karen S. McNeal, 2018. "Individual transportation decisions under conditions of risk and uncertainty," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(2), pages 927-942, June.
    6. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    7. Chen, Roger B., 2018. "Models of count with endogenous choices," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 862-875.
    8. Bhat, Chandra R., 2022. "A closed-form multiple discrete-count extreme value (MDCNTEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 65-86.
    9. Timmermans, Harry & van der Waerden, Peter & Alves, Mario & Polak, John & Ellis, Scott & Harvey, Andrew S. & Kurose, Shigeyuki & Zandee, Rianne, 2002. "Time allocation in urban and transport settings: an international, inter-urban perspective," Transport Policy, Elsevier, vol. 9(2), pages 79-93, April.
    10. Khattak, Asad J. & De Palma, André, 1997. "The impact of adverse weather conditions on the propensity to change travel decisions: A survey of Brussels commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 31(3), pages 181-203, May.

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