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Axis of travel: Modeling non-work destination choice with GPS data

Author

Listed:
  • Arthur (Yan) Huang
  • David Levinson

    (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)

Abstract

Based on in-vehicle GPS travel data in the Minneapolis - St. Paul Metropolitan Area, this research investigates how land use, road network structure, and route fa- miliarity influence home-based single-destination choice. We propose a new choice set formation approach which combines survival analysis and random selection. Our empirical findings reveal that: (1) Walkable opportunities and diversity of services at the destination influence destination choice. (2) Route-specific network measures such as turn index and speed discontinuity display statistically significant effects on destination choice. (3) The familiarity factors reflected by distance to home, work, and downtown also plays a role. A destination closer to home and work, all else equal, is more likely to be selected. A destination farther away from downtown is more attractive for auto users. This research contributes to methodologies in modeling des- tination choice using GPS data. The results enhance our understanding of non-work travel behavior and have implications for transportation and land use planning.

Suggested Citation

  • Arthur (Yan) Huang & David Levinson, 2015. "Axis of travel: Modeling non-work destination choice with GPS data," Working Papers 000113, University of Minnesota: Nexus Research Group.
    • Handle: RePEc:nex:wpaper:axisoftravel
    as

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    File URL: http://hdl.handle.net/11299/180058
    File Function: Second version, 2015
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Calastri, Chiara & Hess, Stephane & Choudhury, Charisma & Daly, Andrew & Gabrielli, Lorenzo, 2019. "Mode choice with latent availability and consideration: Theory and a case study," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 374-385.
    2. Roberto Ponce Lopez & Joseph Ferreira, 2021. "Identifying spatio-temporal hotspots of human activity that are popular non-work destinations," Environment and Planning B, , vol. 48(3), pages 433-448, March.
    3. Zuo, Ting & Wei, Heng & Liu, Hao & Yang, Y. Jeffrey, 2019. "Bi-level optimization approach for configuring population and employment distributions with minimized vehicle travel demand," Journal of Transport Geography, Elsevier, vol. 74(C), pages 161-172.
    4. Leite Mariante, Gabriel & Ma, Tai-Yu & Van Acker, Véronique, 2018. "Modeling discretionary activity location choice using detour factors and sampling of alternatives for mixed logit models," Journal of Transport Geography, Elsevier, vol. 72(C), pages 151-165.
    5. Berjisian, Elmira & Habibian, Meeghat, 2019. "Developing a pedestrian destination choice model using the stratified importance sampling method," Journal of Transport Geography, Elsevier, vol. 77(C), pages 39-47.
    6. Yuji Yoshimura & Stanislav Sobolevsky & Juan N Bautista Hobin & Carlo Ratti & Josep Blat, 2018. "Urban association rules: Uncovering linked trips for shopping behavior," Environment and Planning B, , vol. 45(2), pages 367-385, March.
    7. Tsoleridis, Panagiotis & Choudhury, Charisma F. & Hess, Stephane, 2022. "Deriving transport appraisal values from emerging revealed preference data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 225-245.
    8. Yang Cao & Linxing Wang & Hao Wu & Shuqi Yan & Shuwen Shen, 2023. "Identification and Mechanism of Residents’ Regional Non-Commuting Flow Patterns Based on the Gradient Boosting Decision Tree Model: A Case Study of the Shanghai Metropolitan Area," Land, MDPI, vol. 12(9), pages 1-21, August.
    9. Huang, Arthur & Levinson, David, 2017. "A model of two-destination choice in trip chains with GPS data," Journal of choice modelling, Elsevier, vol. 24(C), pages 51-62.
    10. Zong, Fang & Tian, Yongda & He, Yanan & Tang, Jinjun & Lv, Jianyu, 2019. "Trip destination prediction based on multi-day GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 258-269.
    11. David Levinson, 2022. "Optimum Stop Spacing for Accessibility," Working Papers 2021-08, University of Minnesota: Nexus Research Group.
    12. Pan, Haozhi & Deal, Brian & Chen, Yan & Hewings, Geoffrey, 2018. "A Reassessment of urban structure and land-use patterns: distance to CBD or network-based? — Evidence from Chicago," Regional Science and Urban Economics, Elsevier, vol. 70(C), pages 215-228.
    13. Md. Sakoat Hossan & Hamidreza Asgari & Xia Jin, 2018. "Trip misreporting forecast using count data model in a GPS enhanced travel survey," Transportation, Springer, vol. 45(6), pages 1687-1700, November.
    14. La Paix Puello, Lissy & Chowdhury, Saidul & Geurs, Karst, 2019. "Using panel data for modelling duration dynamics of outdoor leisure activities," Journal of choice modelling, Elsevier, vol. 31(C), pages 141-155.
    15. Hao Wu & David Levinson, 2018. "Optimum Stop Spacing for Accessibility," Working Papers 171, University of Minnesota: Nexus Research Group.

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    More about this item

    Keywords

    Circuity; Accessibility; Transit networks; Network efficiency; Mode share; Public transportation;
    All these keywords.

    JEL classification:

    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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