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Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

Author

Listed:
  • Ruone Zhang

    (Key Laboratory of Road and Traffic Engineering of Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Xin Ye

    (Key Laboratory of Road and Traffic Engineering of Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Ke Wang

    (Key Laboratory of Road and Traffic Engineering of Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Dongjin Li

    (Key Laboratory of Road and Traffic Engineering of Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Jiayu Zhu

    (Key Laboratory of Road and Traffic Engineering of Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

Abstract

Travel data collection, which is necessary for travel demand modeling, is always of great concern to modelers due to its huge cost and effort when a large sample is required to achieve satisfactory model precisions. In this paper, travel data collected based on a survey questionnaire and travelers’ active participation are called actively collected data (ACD). It is difficult to guarantee absolute randomness and unbiasedness in a sample when the ACD are collected due to self-selection issues. The aim of this study is to improve the model precision at low cost by using passively collected data (PCD), such as in-vehicle GPS data and transit smart card data, to release sample size restriction and reduce sampling bias of ACD in a commute mode choice model. In an empirical study, a multinomial-logit-based joint model is developed for commute mode choice by integrating ACD and PCD based on the choice-based sampling theory. A comprehensive set of explanatory variables are specified through data integration. Both simulation and empirical results show great improvement in coefficient precisions in the proposed joint model, relative to those in the ACD model and PCD model. In this study, ACD and PCD samples of Shanghai are integrated in the joint model so that several significantly influential level-of-service attributes are identified for auto, rail, and bus modes, and their impacts on commute mode choice probabilities are quantified. The findings can aid in better evaluating the program to improve the existing transit system.

Suggested Citation

  • Ruone Zhang & Xin Ye & Ke Wang & Dongjin Li & Jiayu Zhu, 2019. "Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2730-:d:230845
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    References listed on IDEAS

    as
    1. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    2. Bhat, Chandra R., 1998. "Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 495-507, September.
    3. Tang, Jinjun & Liang, Jian & Zhang, Shen & Huang, Helai & Liu, Fang, 2018. "Inferring driving trajectories based on probabilistic model from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 566-577.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    5. Ahmed Osman Idris & Khandker M. Nurul Habib & Alejandro Tudela & Amer Shalaby, 2015. "Investigating the effects of psychological factors on commuting mode choice behaviour," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(3), pages 265-276, April.
    6. Yajie Zou & Xinzhi Zhong & Jinjun Tang & Xin Ye & Lingtao Wu & Muhammad Ijaz & Yinhai Wang, 2019. "A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    7. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    8. Yajie Zou & John E. Ash & Byung-Jung Park & Dominique Lord & Lingtao Wu, 2018. "Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1652-1669, July.
    9. Yu Liu & Chaogui Kang & Song Gao & Yu Xiao & Yuan Tian, 2012. "Understanding intra-urban trip patterns from taxi trajectory data," Journal of Geographical Systems, Springer, vol. 14(4), pages 463-483, October.
    10. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-1988, November.
    11. Changxi Ma & Ruichun He & Wei Zhang, 2018. "Path optimization of taxi carpooling," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-15, August.
    12. Kamargianni, Maria & Dubey, Subodh & Polydoropoulou, Amalia & Bhat, Chandra, 2015. "Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 473-488.
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    Cited by:

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    3. Tang, Jinjun & Hu, Jin & Hao, Wei & Chen, Xinqiang & Qi, Yong, 2020. "Markov Chains based route travel time estimation considering link spatio-temporal correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    4. Kaniz Fatima & Sara Moridpour & Chris De Gruyter & Tayebeh Saghapour, 2020. "Elderly Sustainable Mobility: Scientific Paper Review," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    5. Ke Wang & Chandra R. Bhat & Xin Ye, 2023. "A multinomial probit analysis of shanghai commute mode choice," Transportation, Springer, vol. 50(4), pages 1471-1495, August.
    6. Yong Gao & Chao Ye & Xiang Zhong & Lun Wu & Yu Liu, 2019. "Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs," Sustainability, MDPI, vol. 11(13), pages 1-18, June.
    7. Xin Guan & Xin Ye & Cheng Shi & Yajie Zou, 2019. "A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China," Sustainability, MDPI, vol. 11(20), pages 1-19, October.

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