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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

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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.

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Notes

  1. An at-work sub-tour is defined as a sequence of trips that start and end at the primary workplace (for example, all lunch trips at work place constitute an at-work sub-tour).

  2. Please note that the Rank Ordered Logit (ROL) model used in this study is different from the ordered logit model used for analyzing ordinal variables (Paleti and Bhat 2013).

  3. To test scale heterogeneity, we used a nested logit specification where the choice set was duplicated for each sub-sample and placed under a separate nest with a linkage across all model coefficients except for the scale parameters. Polydoropoulou and Ben-Akiva (2001) and Paleti et al. (2014) used similar techniques for uncovering scale heterogeneity in revealed and stated preference datasets. However, both unconstrained optimization of the resulting log-likelihood function and grid search over an extended range of scale parameters did not indicate scale parameters significantly different from 1.

  4. Deviation is calculated as follows: (OH + HD − OD) where OH is Origin-to-Home distance, HD is Home-to Destination distance, and OD is Origin-to-Destination distance.

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Acknowledgments

The authors would like to thank four anonymous reviewers whose comments helped improve an earlier version of the paper considerably.

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Authors and Affiliations

  1. Department of Civil & Environmental Engineering, Old Dominion University, 135 Kaufman Hall, Norfolk, VA, 23529, USA

    Rajesh Paleti

  2. Parsons Brinckerhoff, 1 Penn Plaza, 3rd Floor, New York, NY, 10119, USA

    Peter Vovsha

  3. Parsons Brinckerhoff, 1 Penn Plaza, 2nd Floor, New York, NY, 10119, USA

    Gaurav Vyas

  4. Ohio Department of Transportation, 1980 W. Broad St., Columbus, OH, 43223, USA

    Rebekah Anderson & Gregory Giaimo

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  1. Rajesh Paleti
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  2. Peter Vovsha
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  3. Gaurav Vyas
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  4. Rebekah Anderson
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  5. Gregory Giaimo
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Correspondence to Rajesh Paleti.

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Paleti, R., Vovsha, P., Vyas, G. et al. Activity sequencing, location, and formation of individual non-mandatory tours: application to the activity-based models for Columbus, Cincinnati, and Cleveland, OH. Transportation 44, 615–640 (2017). https://doi.org/10.1007/s11116-015-9671-5

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  • DOI: https://doi.org/10.1007/s11116-015-9671-5

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