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Multilevel modelling of commercial vehicle inter-arrival duration using GPS data

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  • Sharman, Bryce W.
  • Roorda, Matthew J.

Abstract

This study uses truck GPS data to study inter-arrival duration, defined as the time between arrivals at a destination of two successive vehicles operated by the same carrier. Destinations were separated into market segments: (1) frequently-visited destinations, (2) regularly-scheduled destinations, and (3) unscheduled destinations, based on visit frequency and regularity. Inter-arrival duration was modelled using multilevel ordered probit and multilevel multinomial logit models. Market segmentation improved modelling results, and multilevel models performed better than single level models. Results showed a wide variation in shipping behavior of commercial establishments and that many firms do not follow consistent shipping schedules.

Suggested Citation

  • Sharman, Bryce W. & Roorda, Matthew J., 2013. "Multilevel modelling of commercial vehicle inter-arrival duration using GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 94-107.
  • Handle: RePEc:eee:transe:v:56:y:2013:i:c:p:94-107
    DOI: 10.1016/j.tre.2013.06.002
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    References listed on IDEAS

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    Cited by:

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    2. Demissie, Merkebe Getachew & Kattan, Lina, 2022. "Estimation of truck origin-destination flows using GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    3. Laranjeiro, Patrícia F. & Merchán, Daniel & Godoy, Leonardo A. & Giannotti, Mariana & Yoshizaki, Hugo T.Y. & Winkenbach, Matthias & Cunha, Claudio B., 2019. "Using GPS data to explore speed patterns and temporal fluctuations in urban logistics: The case of São Paulo, Brazil," Journal of Transport Geography, Elsevier, vol. 76(C), pages 114-129.
    4. Johan Joubert & Sumarie Meintjes, 2015. "Computational considerations in building inter-firm networks," Transportation, Springer, vol. 42(5), pages 857-878, September.
    5. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Li, Jiangtao & Yang, Zhenzhen & Gao, Ziyou, 2022. "Identifying intercity freight trip ends of heavy trucks from GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    6. Piendl, Raphael & Liedtke, Gernot & Matteis, Tilman, 2017. "A logit model for shipment size choice with latent classes – Empirical findings for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 188-201.

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