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The impacts of congestion on commercial vehicle tour characteristics and costs

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  • Figliozzi, Miguel Andres

Abstract

Analytical modeling and insights, numerical experiments, and real-world tour data are used to understand the impact of congestion on urban tour characteristics, carriers' costs, and distance/time traveled. This paper categorizes tours into three classes based on their tour efficiency and variable costs structure. Travel time/distance between customers and depot is found to be a crucial factor that exacerbates the negative impacts of congestion. Travel time variability is a significant factor only when travel time between depot and customers is considerable in relation to the maximum tour duration. For each customer, it is possible to define a dimensionless coefficient that provides an indication of the relative impact of congestion on routing constraints. Congestion also affects carriers' cost structure, as congestion worsens the relative weight of wages and overtime escalates and the relative weight of distance related costs decrease.

Suggested Citation

  • Figliozzi, Miguel Andres, 2010. "The impacts of congestion on commercial vehicle tour characteristics and costs," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(4), pages 496-506, July.
    • Handle: RePEc:eee:transe:v:46:y:2010:i:4:p:496-506
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    Cited by:

    1. Christian Fikar & Patrick Hirsch, 2018. "Evaluation of trip and car sharing concepts for home health care services," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 78-97, June.
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    3. Klauenberg, Jens & Elsner, Lucas-Andrés & Knischewski, Christian, 2020. "Dynamics of the spatial distribution of hubs in groupage networks – The case of Berlin," Journal of Transport Geography, Elsevier, vol. 88(C).
    4. Güner, Ali R. & Murat, Alper & Chinnam, Ratna Babu, 2017. "Dynamic routing for milk-run tours with time windows in stochastic time-dependent networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 251-267.
    5. Bock, Stefan, 2020. "Optimally solving a versatile Traveling Salesman Problem on tree networks with soft due dates and multiple congestion scenarios," European Journal of Operational Research, Elsevier, vol. 283(3), pages 863-882.
    6. Anna Corinna Cagliano & Alberto Marco & Giulio Mangano & Giovanni Zenezini, 2017. "Levers of logistics service providers’ efficiency in urban distribution," Operations Management Research, Springer, vol. 10(3), pages 104-117, December.
    7. Ferguson, Mark & Maoh, Hanna & Ryan, Justin & Kanaroglou, Pavlos & Rashidi, Taha Hossein, 2012. "Transferability and enhancement of a microsimulation model for estimating urban commercial vehicle movements," Journal of Transport Geography, Elsevier, vol. 24(C), pages 358-369.
    8. Roberto Tadei & Guido Perboli & Francesca Perfetti, 2017. "The multi-path Traveling Salesman Problem with stochastic travel costs," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 3-23, March.
    9. Sjoerd van der Spoel & Chintan Amrit & Jos van Hillegersberg, 2017. "Predictive analytics for truck arrival time estimation: a field study at a European distribution centre," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5062-5078, September.
    10. Anna Franceschetti & Ola Jabali & Gilbert Laporte, 2017. "Continuous approximation models in freight distribution management," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 413-433, October.
    11. Amaya, Johanna & Delgado-Lindeman, Maira & Arellana, Julian & Allen, Jaime, 2021. "Urban freight logistics: What do citizens perceive?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    12. Wei Zhou & Jane Lin, 2019. "An On-Demand Same-Day Delivery Service Using Direct Peer-to-Peer Transshipment Strategies," Networks and Spatial Economics, Springer, vol. 19(2), pages 409-443, June.
    13. Carrese, Stefano & Cuneo, Valerio & Nigro, Marialisa & Pizzuti, Raffaele & Ardito, Cosimo Federico & Marseglia, Guido, 2022. "Optimization of downstream fuel logistics based on road infrastructure conditions and exposure to accident events," Transport Policy, Elsevier, vol. 124(C), pages 96-105.
    14. Behiri, Walid & Belmokhtar-Berraf, Sana & Chu, Chengbin, 2018. "Urban freight transport using passenger rail network: Scientific issues and quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 227-245.
    15. Vidal Vieira, José Geraldo & Fransoo, Jan C., 2015. "How logistics performance of freight operators is affected by urban freight distribution issues," Transport Policy, Elsevier, vol. 44(C), pages 37-47.
    16. Mahdi Alinaghian & Komail Zamanlou & Mohammad S. Sabbagh, 2017. "A bi-objective mathematical model for two-dimensional loading time-dependent vehicle routing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1422-1441, November.
    17. Ozturk, Onur & Patrick, Jonathan, 2018. "An optimization model for freight transport using urban rail transit," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1110-1121.
    18. Andrés Gómez & Ricardo Mariño & Raha Akhavan-Tabatabaei & Andrés L. Medaglia & Jorge E. Mendoza, 2016. "On Modeling Stochastic Travel and Service Times in Vehicle Routing," Transportation Science, INFORMS, vol. 50(2), pages 627-641, May.
    19. Jianjun Dong & Yuanxian Xu & Bon-gang Hwang & Rui Ren & Zhilong Chen, 2019. "The Impact of Underground Logistics System on Urban Sustainable Development: A System Dynamics Approach," Sustainability, MDPI, vol. 11(5), pages 1-21, February.

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