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Activity sequencing, location, and formation of individual non-mandatory tours: application to the activity-based models for Columbus, Cincinnati, and Cleveland, OH

Author

Listed:
  • Rajesh Paleti

    (Old Dominion University)

  • Peter Vovsha

    (Parsons Brinckerhoff)

  • Gaurav Vyas

    (Parsons Brinckerhoff)

  • Rebekah Anderson

    (Ohio Department of Transportation)

  • Gregory Giaimo

    (Ohio Department of Transportation)

Abstract

Most of the earlier activity based models (ABMs) largely relied on a tour-based modeling paradigm which explicitly predicts tour frequency and then adds details including stop frequency, order, and location of stops within each tour. The current study is part of new tour formation design framework for an ABM in which the underlying tour structure and the stop frequency within tours emerge from temporal, sequencing, and locational preferences of activities that the traveler intends to participate during the day. In order to do this, the study developed a modified rank-ordered logit (ROL) framework that is capable of modeling sequence, locations, as well as the underlying tour structure of all activity episodes simultaneously in an integrated manner. Model estimation with the household survey data, provided several important behavioral insights into underlying choices that drive tour formation. Specifically, the study uncovered pairwise ordering preferences among episodes of different activity purposes, clustering tendencies among episodes of same activity purpose, the impact of supply side activity opportunities on the location and sequence choice dimensions, and impedance effects (including distance and mode and time-of-day logsums) on location and tour break dimensions. The developed models are incorporated in the operational ABM structure adopted for three major cities (Columbus, Cleveland, and Cincinnati) in Ohio.

Suggested Citation

  • Rajesh Paleti & Peter Vovsha & Gaurav Vyas & Rebekah Anderson & Gregory Giaimo, 2017. "Activity sequencing, location, and formation of individual non-mandatory tours: application to the activity-based models for Columbus, Cincinnati, and Cleveland, OH," Transportation, Springer, vol. 44(3), pages 615-640, May.
    • Handle: RePEc:kap:transp:v:44:y:2017:i:3:d:10.1007_s11116-015-9671-5
    DOI: 10.1007/s11116-015-9671-5
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    References listed on IDEAS

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    Cited by:

    1. Gaurav Vyas & Pooneh Famili & Peter Vovsha & Daniel Fay & Ashish Kulshrestha & Greg Giaimo & Rebekah Anderson, 2019. "Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus, OH," Transportation, Springer, vol. 46(6), pages 2081-2102, December.
    2. 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.
    3. Dianat, Leila & Habib, Khandker Nurul & Miller, Eric J., 2020. "Modeling and forecasting daily non-work/school activity patterns in an activity-based model using skeleton schedule constraints," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 337-352.
    4. Leila Dianat & Khandker Nurul Habib & Eric J. Miller, 2020. "Investigating the influence of assigning a higher priority to scheduling work and school activities in the activity-based models on the simulated travel/activity patterns," Transportation, Springer, vol. 47(5), pages 2109-2132, October.
    5. 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.
    6. Ge Gao & Huijun Sun & Jianjun Wu, 2019. "Activity-based trip chaining behavior analysis in the network under the parking fee scheme," Transportation, Springer, vol. 46(3), pages 647-669, June.

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