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Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data

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  • Antonello Ignazio Croce

    (Dipartimento di Agraria, Università Mediterranea di Reggio Calabria, 89122 Reggio Calabria, Italy)

  • Giuseppe Musolino

    (Dipartimento di Ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Università Mediterranea di Reggio Calabria, 89122 Reggio Calabria, Italy)

  • Corrado Rindone

    (Dipartimento di Ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Università Mediterranea di Reggio Calabria, 89122 Reggio Calabria, Italy)

  • Antonino Vitetta

    (Dipartimento di Ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Università Mediterranea di Reggio Calabria, 89122 Reggio Calabria, Italy)

Abstract

According to the literature, the path choice decision process of a user of a (road) transport network, named path choice problem (PCP), is composed of two levels/models: the definition of perceived alternative paths (choice set) and the choice of one path in the path choice set. The path choice probability can be estimated with two models: a choice model of the path choice set and a choice model of a path (Mansky paradigm). In this research, the paper’s contribution concerns two elements: extension of the PCP paradigm (two-level models) consolidated in the literature to the route choice decision process (vehicle routing problem (VRP)) and identification of common elements in the PCP and VRP concerning the criteria in the two decision levels and the procedure for route and path selection and choice. The experiment concerns the comparison of observed routes with simulated and optimized routes of commercial vehicles to analyse the level of similarity and coverage. The observed routes are extracted from floating car data (FCD) from commercial vehicles travelling inside a study area inside the Calabria Region (Southern Italy). The comparison is executed in terms of similarity of the sequences of nodes visited between observed routes and simulated/optimized routes.

Suggested Citation

  • Antonello Ignazio Croce & Giuseppe Musolino & Corrado Rindone & Antonino Vitetta, 2020. "Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8557-:d:429018
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    References listed on IDEAS

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    1. Majid Salavati-Khoshghalb & Michel Gendreau & Ola Jabali & Walter Rei, 2019. "A hybrid recourse policy for the vehicle routing problem with stochastic demands," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 269-298, September.
    2. Majid Salavati-Khoshghalb & Michel Gendreau & Ola Jabali & Walter Rei, 2019. "A Rule-Based Recourse for the Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 53(5), pages 1334-1353, September.
    3. Jin Y. Yen, 1971. "Finding the K Shortest Loopless Paths in a Network," Management Science, INFORMS, vol. 17(11), pages 712-716, July.
    4. Éric Taillard & Philippe Badeau & Michel Gendreau & François Guertin & Jean-Yves Potvin, 1997. "A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows," Transportation Science, INFORMS, vol. 31(2), pages 170-186, May.
    5. Russo, Francesco & Musolino, Giuseppe, 2012. "A unifying modelling framework to simulate the Spatial Economic Transport Interaction process at urban and national scales," Journal of Transport Geography, Elsevier, vol. 24(C), pages 189-197.
    6. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert, 2019. "The electric vehicle routing problem with energy consumption uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 225-255.
    7. Jesus Gonzalez-Feliu & Pascal Pluvinet & Marc Serouge & Mathieu Gardrat, 2013. "GPS-based data production in urban freight distribution," Working Papers halshs-00784079, HAL.
    8. Gilbert Laporte, 2007. "What you should know about the vehicle routing problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(8), pages 811-819, December.
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    Cited by:

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