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Modelling trip generation using mobile phone data: A latent demographics approach

Author

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  • Bwambale, Andrew
  • Choudhury, Charisma F.
  • Hess, Stephane

Abstract

Traditional approaches to trip generation modelling rely on household travel surveys which are expensive and prone to reporting errors. On the other hand, mobile phone data, where spatio-temporal trajectories of millions of users are passively recorded has recently emerged as a promising input for transport analyses. However, such data has primarily been used for the development of human mobility models, extraction of statistics on human mobility behaviour, and origin-destination matrix estimation as opposed to the development of econometric models of travel demand. This is primarily due to the exclusion of user demographics from mobile phone data made available for research (owing to privacy reasons). In this study, we address this limitation by proposing a hybrid trip generation model framework where demographic groups are treated as latent or unobserved. The proposed model first predicts the demographic group membership probabilities of individuals based on their phone usage characteristics and then uses these probabilities as weights inside a latent class model for trip generation, with different classes representing different socio-demographic groups. The model is calibrated using the call log data of a sub-sample of users with known demographics and trip rates extracted from their GSM mobility data. The performance of the hybrid model is compared with that of a traditional trip generation model which uses observed demographic variables to validate the proposed methodology. This comparative analysis shows that the model fit and the prediction results of the hybrid model are close to those of the traditional model. The research thus serves as a proof-of-concept that the mobile phone data can be successfully used to develop econometric models of transport planning by having additional information for a subset of the users.

Suggested Citation

  • Bwambale, Andrew & Choudhury, Charisma F. & Hess, Stephane, 2019. "Modelling trip generation using mobile phone data: A latent demographics approach," Journal of Transport Geography, Elsevier, vol. 76(C), pages 276-286.
  • Handle: RePEc:eee:jotrge:v:76:y:2019:i:c:p:276-286
    DOI: 10.1016/j.jtrangeo.2017.08.020
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    References listed on IDEAS

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    1. Vickerman, R. W. & Barmby, T. A., 1985. "Household trip generation choice--Alternative empirical approaches," Transportation Research Part B: Methodological, Elsevier, vol. 19(6), pages 471-479, December.
    2. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
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    Cited by:

    1. Wadud, Zia & Mattioli, Giulio, 2021. "Fully automated vehicles: A cost-based analysis of the share of ownership and mobility services, and its socio-economic determinants," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 228-244.
    2. Zhao, Yuanying & Pawlak, Jacek & Sivakumar, Aruna, 2022. "Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 101-134.
    3. Burcu Ozgun & Tom Broekel, 2024. "Saved by the news? COVID-19 in German news and its relationship with regional mobility behaviour," Regional Studies, Taylor & Francis Journals, vol. 58(2), pages 365-380, February.
    4. Andrew Bwambale & Charisma F. Choudhury & Stephane Hess & Md. Shahadat Iqbal, 2021. "Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling," Transportation, Springer, vol. 48(5), pages 2287-2314, October.
    5. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).

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