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Modeling the Commute Activity-Travel Pattern of Workers: Formulation and Empirical Analysis

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  • Chandra Bhat

    (Department of Civil Engineering, ECJ 6.810, University of Texas at Austin, Austin, Texas 78712)

Abstract

This paper proposes a methodological framework to analyze the activity and travel pattern of workers during the evening commute. The framework uses a discrete-continuous econometric system to model jointly the decision to participate in an activity during the evening commute and the following attributes of the participation: activity type, activity duration, and travel time deviation to the activity location relative to the direct travel time from work to home. The model parameters are estimated using a sample of workers from the 1991 Boston Household Activity Survey. The paper also presents mathematical expressions to evaluate the effect of changes in sociodemographic variables and policy-relevant exogenous variables on the temporal pattern of trips and cold starts attributable to commute stops. The application of the model indicates that failure to accommodate the joint nature of the activity decisions during the evening commute can lead to misdirected policy actions for traffic congestion alleviation and for mobile-source emissions reduction.

Suggested Citation

  • Chandra Bhat, 2001. "Modeling the Commute Activity-Travel Pattern of Workers: Formulation and Empirical Analysis," Transportation Science, INFORMS, vol. 35(1), pages 61-79, February.
    • Handle: RePEc:inm:ortrsc:v:35:y:2001:i:1:p:61-79
    DOI: 10.1287/trsc.35.1.61.10142
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    References listed on IDEAS

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    1. Mohammad M. Hamed & Fred L. Mannering, 1993. "Modeling Travelers' Postwork Activity Involvement: Toward a New Methodology," Transportation Science, INFORMS, vol. 27(4), pages 381-394, November.
    2. Lee, Lung-Fei, 1983. "Generalized Econometric Models with Selectivity," Econometrica, Econometric Society, vol. 51(2), pages 507-512, March.
    3. Bhat, Chandra R. & Singh, Sujit K., 2000. "A comprehensive daily activity-travel generation model system for workers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(1), pages 1-22, January.
    4. Bhat, Chandra R., 1996. "A hazard-based duration model of shopping activity with nonparametric baseline specification and nonparametric control for unobserved heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 30(3), pages 189-207, June.
    5. Kondo, Katsunao & Kitamura, Ryuichi, 1987. "Time-space constraints and the formation of trip chains," Regional Science and Urban Economics, Elsevier, vol. 17(1), pages 49-65, February.
    6. Bhat, Chandra R., 1998. "A model of post home-arrival activity participation behavior," Transportation Research Part B: Methodological, Elsevier, vol. 32(6), pages 387-400, August.
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    Citations

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    Cited by:

    1. Bautista-Hernández, Dorian Antonio, 2022. "Individual, household, and urban form determinants of trip chaining of non-work travel in México City," Journal of Transport Geography, Elsevier, vol. 98(C).
    2. Jingni Song & Feng Chen & Qunqi Wu & Weiyu Liu & Feiyang Xue & Kai Du, 2019. "Optimization of Passenger Transportation Corridor Mode Supply Structure in Regional Comprehensive Transport Considering Economic Equilibrium," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
    3. Ta, Na & Zhao, Ying & Chai, Yanwei, 2016. "Built environment, peak hours and route choice efficiency: An investigation of commuting efficiency using GPS data," Journal of Transport Geography, Elsevier, vol. 57(C), pages 161-170.
    4. Calastri, Chiara & Hess, Stephane & Daly, Andrew & Carrasco, Juan Antonio, 2017. "Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 1-20.
    5. Usman Ahmed & Ana Tsui Moreno & Rolf Moeckel, 2021. "Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes," Transportation, Springer, vol. 48(3), pages 1481-1502, June.
    6. Jianchuan Xianyu & Soora Rasouli & Harry Timmermans, 2017. "Analysis of variability in multi-day GPS imputed activity-travel diaries using multi-dimensional sequence alignment and panel effects regression models," Transportation, Springer, vol. 44(3), pages 533-553, May.
    7. Rongrong Hong & Wenming Rao & Dong Zhou & Chengchuan An & Zhenbo Lu & Jingxin Xia, 2020. "Commuting Pattern Recognition Using a Systematic Cluster Framework," Sustainability, MDPI, vol. 12(5), pages 1-20, February.
    8. Usman Ahmed & Ana Tsui Moreno & Rolf Moeckel, 0. "Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes," Transportation, Springer, vol. 0, pages 1-22.
    9. Stephan Brunow & Manuela Gründer, 2013. "The impact of activity chaining on the duration of daily activities," Transportation, Springer, vol. 40(5), pages 981-1001, September.
    10. Irene Casas & Mei‐Po Kwan, 2007. "The Impact of Real‐Time Information on Choices During the Commute Trip: Evidence from a Travel Simulator," Growth and Change, Wiley Blackwell, vol. 38(4), pages 523-543, December.

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