IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i20p8557-d429018.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/20/8557/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/20/8557/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. Jesus Gonzalez-Feliu & Pascal Pluvinet & Marc Serouge & Mathieu Gardrat, 2013. "GPS-based data production in urban freight distribution," Working Papers halshs-00784079, HAL.
    4. 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.
    5. Jin Y. Yen, 1971. "Finding the K Shortest Loopless Paths in a Network," Management Science, INFORMS, vol. 17(11), pages 712-716, July.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Serkan Alacam & Asli Sencer, 2021. "Using Blockchain Technology to Foster Collaboration among Shippers and Carriers in the Trucking Industry: A Design Science Research Approach," Logistics, MDPI, vol. 5(2), pages 1-24, June.
    2. Cesar Eduardo Leite & Sérgio Ronaldo Granemann & Ari Melo Mariano & Leise Kelli de Oliveira, 2022. "Opinion of Residents about the Freight Transport and Its Influence on the Quality of Life: An Analysis for Brasília (Brazil)," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    3. Antonio Comi & Antonio Polimeni, 2022. "Estimating Path Choice Models through Floating Car Data," Forecasting, MDPI, vol. 4(2), pages 1-13, June.
    4. Fatemeh Nourmohammadi & Mohammadhadi Mansourianfar & Sajjad Shafiei & Ziyuan Gu & Meead Saberi, 2021. "An Open GMNS Dataset of a Dynamic Multi-Modal Transportation Network Model of Melbourne, Australia," Data, MDPI, vol. 6(2), pages 1-9, February.
    5. Antonello Ignazio Croce & Giuseppe Musolino & Corrado Rindone & Antonino Vitetta, 2021. "Estimation of Travel Demand Models with Limited Information: Floating Car Data for Parameters’ Calibration," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    6. Zhenjun Zhu & Jun Zeng & Xiaolin Gong & Yudong He & Shucheng Qiu, 2021. "Analyzing Influencing Factors of Transfer Passenger Flow of Urban Rail Transit: A New Approach Based on Nested Logit Model Considering Transfer Choices," IJERPH, MDPI, vol. 18(16), pages 1-14, August.
    7. Francesco Russo & Antonio Comi, 2021. "Sustainable Urban Delivery: The Learning Process of Path Costs Enhanced by Information and Communication Technologies," Sustainability, MDPI, vol. 13(23), pages 1-13, November.
    8. Hanghun Jo & Heungsoon Kim, 2021. "Developing a Traffic Model to Estimate Vehicle Emissions: An Application in Seoul, Korea," Sustainability, MDPI, vol. 13(17), pages 1-18, August.
    9. Yinghan Zhu & Liudan Jiao & Yu Zhang & Ya Wu & Xiaosen Huo, 2021. "Sustainable Development of Urban Metro System: Perspective of Coordination between Supply and Demand," IJERPH, MDPI, vol. 18(19), pages 1-24, September.
    10. Jiekun Song & Xiaoping Ma & Rui Chen, 2021. "A Profit Distribution Model of Reverse Logistics Based on Fuzzy DEA Efficiency—Modified Shapley Value," Sustainability, MDPI, vol. 13(13), pages 1-20, June.
    11. Yangyang Ma & Pengyu Wang & Tianjun Sun, 2021. "Research on Energy Management Method of Plug-In Hybrid Electric Vehicle Based on Travel Characteristic Prediction," Energies, MDPI, vol. 14(19), pages 1-17, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lai, Kexing & Chen, Tao & Natarajan, Balasubramaniam, 2020. "Optimal scheduling of electric vehicles car-sharing service with multi-temporal and multi-task operation," Energy, Elsevier, vol. 204(C).
    2. Stanisław Iwan & Mariusz Nürnberg & Artur Bejger & Kinga Kijewska & Krzysztof Małecki, 2021. "Unloading Bays as Charging Stations for EFV-Based Urban Freight Delivery System—Example of Szczecin," Energies, MDPI, vol. 14(18), pages 1-22, September.
    3. Maximiliano Cubillos & Mauro Dell’Amico & Ola Jabali & Federico Malucelli & Emanuele Tresoldi, 2023. "An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences," Energies, MDPI, vol. 16(10), pages 1-19, May.
    4. Nicholas D. Kullman & Aurelien Froger & Jorge E. Mendoza & Justin C. Goodson, 2021. "frvcpy: An Open-Source Solver for the Fixed Route Vehicle Charging Problem," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1277-1283, October.
    5. Hamid R. Sayarshad & Vahid Mahmoodian & Nebojša Bojović, 2021. "Dynamic Inventory Routing and Pricing Problem with a Mixed Fleet of Electric and Conventional Urban Freight Vehicles," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    6. Sergio Maria Patella & Gianluca Grazieschi & Valerio Gatta & Edoardo Marcucci & Stefano Carrese, 2020. "The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review," Sustainability, MDPI, vol. 13(1), pages 1-29, December.
    7. Florio, Alexandre M. & Gendreau, Michel & Hartl, Richard F. & Minner, Stefan & Vidal, Thibaut, 2023. "Recent advances in vehicle routing with stochastic demands: Bayesian learning for correlated demands and elementary branch-price-and-cut," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1081-1093.
    8. Mohamed Cissé & Semih Yalçindag & Yannick Kergosien & Evren Sahin & Christophe Lenté & Andrea Matta, 2017. "OR problems related to Home Health Care: A review of relevant routing and scheduling problems," Post-Print hal-01736714, HAL.
    9. De La Vega, Jonathan & Gendreau, Michel & Morabito, Reinaldo & Munari, Pedro & Ordóñez, Fernando, 2023. "An integer L-shaped algorithm for the vehicle routing problem with time windows and stochastic demands," European Journal of Operational Research, Elsevier, vol. 308(2), pages 676-695.
    10. Huili Zhang & Yinfeng Xu & Xingang Wen, 2015. "Optimal shortest path set problem in undirected graphs," Journal of Combinatorial Optimization, Springer, vol. 29(3), pages 511-530, April.
    11. Sébastien Mouthuy & Florence Massen & Yves Deville & Pascal Van Hentenryck, 2015. "A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows," Transportation Science, INFORMS, vol. 49(2), pages 223-238, May.
    12. Schmid, Verena & Doerner, Karl F. & Laporte, Gilbert, 2013. "Rich routing problems arising in supply chain management," European Journal of Operational Research, Elsevier, vol. 224(3), pages 435-448.
    13. Raimbault, Juste & Le Néchet, Florent, 2021. "Introducing endogenous transport provision in a LUTI model to explore polycentric governance systems," Journal of Transport Geography, Elsevier, vol. 94(C).
    14. Daria Dzyabura & Srikanth Jagabathula, 2018. "Offline Assortment Optimization in the Presence of an Online Channel," Management Science, INFORMS, vol. 64(6), pages 2767-2786, June.
    15. Chiang, Wen-Chyuan & Russell, Robert & Xu, Xiaojing & Zepeda, David, 2009. "A simulation/metaheuristic approach to newspaper production and distribution supply chain problems," International Journal of Production Economics, Elsevier, vol. 121(2), pages 752-767, October.
    16. Melchiori, Anna & Sgalambro, Antonino, 2020. "A branch and price algorithm to solve the Quickest Multicommodity k-splittable Flow Problem," European Journal of Operational Research, Elsevier, vol. 282(3), pages 846-857.
    17. Luss, Hanan & Wong, Richard T., 2005. "Graceful reassignment of excessively long communications paths in networks," European Journal of Operational Research, Elsevier, vol. 160(2), pages 395-415, January.
    18. Rinaldi, Marco & Viti, Francesco, 2017. "Exact and approximate route set generation for resilient partial observability in sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 86-119.
    19. Zolfagharinia, Hossein & Haughton, Michael, 2018. "The importance of considering non-linear layover and delay costs for local truckers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 331-355.
    20. Calvete, Herminia I. & Gale, Carmen & Oliveros, Maria-Jose & Sanchez-Valverde, Belen, 2007. "A goal programming approach to vehicle routing problems with soft time windows," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1720-1733, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8557-:d:429018. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.