IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v77y2015icp29-44.html
   My bibliography  Save this article

Developing advanced route choice models for heavy goods vehicles using GPS data

Author

Listed:
  • Hess, Stephane
  • Quddus, Mohammed
  • Rieser-Schüssler, Nadine
  • Daly, Andrew

Abstract

This paper presents a novel application in route choice modelling using Global Positioning System (GPS) data, focussing on heavy goods vehicles which typically make longer journeys with decisions potentially underpinned by different priorities from those used by car drivers. The scope of the study is larger than many previous ones, using the entire road network of England. Making use of the error components model put forward for route choice by Frejinger and Bierlaire (2007), the work reveals low elasticities in response to changes in travel time, reflecting the limited opportunity for avoiding specific roads on long distance journeys by heavy goods vehicles.

Suggested Citation

  • Hess, Stephane & Quddus, Mohammed & Rieser-Schüssler, Nadine & Daly, Andrew, 2015. "Developing advanced route choice models for heavy goods vehicles using GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 29-44.
    • Handle: RePEc:eee:transe:v:77:y:2015:i:c:p:29-44
    DOI: 10.1016/j.tre.2015.01.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554515000113
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2015.01.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Menghini, G. & Carrasco, N. & Schüssler, N. & Axhausen, K.W., 2010. "Route choice of cyclists in Zurich," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(9), pages 754-765, November.
    2. van der Zijpp, N.J. & Fiorenzo Catalano, S., 2005. "Path enumeration by finding the constrained K-shortest paths," Transportation Research Part B: Methodological, Elsevier, vol. 39(6), pages 545-563, July.
    3. Quattrone, Agata & Vitetta, Antonino, 2011. "Random and fuzzy utility models for road route choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1126-1139.
    4. Azevedo, JoseAugusto & Santos Costa, Maria Emilia O. & Silvestre Madeira, Joaquim Joao E. R. & Vieira Martins, Ernesto Q., 1993. "An algorithm for the ranking of shortest paths," European Journal of Operational Research, Elsevier, vol. 69(1), pages 97-106, August.
    5. Swait, Joffre, 2001. "Choice set generation within the generalized extreme value family of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 643-666, August.
    6. Frejinger, E. & Bierlaire, M. & Ben-Akiva, M., 2009. "Sampling of alternatives for route choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(10), pages 984-994, December.
    7. Nielsen, Otto Anker, 2000. "A stochastic transit assignment model considering differences in passengers utility functions," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 377-402, June.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, September.
    9. Frejinger, E. & Bierlaire, M., 2007. "Capturing correlation with subnetworks in route choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(3), pages 363-378, March.
    10. Shlomo Bekhor & Moshe Ben-Akiva & M. Ramming, 2006. "Evaluation of choice set generation algorithms for route choice models," Annals of Operations Research, Springer, vol. 144(1), pages 235-247, April.
    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. Calastri, Chiara & Hess, Stephane & Choudhury, Charisma & Daly, Andrew & Gabrielli, Lorenzo, 2019. "Mode choice with latent availability and consideration: Theory and a case study," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 374-385.
    2. Wu, Jishi & Feng, Tao & Jia, Peng & Li, Gen, 2024. "Spatial allocation of heavy commercial vehicles parking areas through geo-fencing," Journal of Transport Geography, Elsevier, vol. 117(C).
    3. Yang, Zhiwei & Chen, Xiaohong & Deng, Jihao & Li, Tianhao & Yuan, Quan, 2023. "Footprints of goods movements: Spatial heterogeneity of heavy-duty truck activities and its influencing factors in the urban context," Journal of Transport Geography, Elsevier, vol. 113(C).
    4. Lili Zheng & Tian Gao & Lin Meng & Tongqiang Ding & Wenhao Chen, 2024. "Research on the Route Choice Behavior of Urban Freight Vehicles Based on GPS Data," Mathematics, MDPI, vol. 12(2), pages 1-17, January.
    5. 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.
    6. Adam, Arnaud & Finance, Olivier & Thomas, Isabelle, 2021. "Monitoring trucks to reveal Belgian geographical structures and dynamics: From GPS traces to spatial interactions," Journal of Transport Geography, Elsevier, vol. 91(C).
    7. Oka, Hideki & Hagino, Yasukatsu & Kenmochi, Takeshi & Tani, Ryota & Nishi, Ryuta & Endo, Kotaro & Fukuda, Daisuke, 2019. "Predicting travel pattern changes of freight trucks in the Tokyo Metropolitan area based on the latest large-scale urban freight survey and route choice modeling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 305-324.
    8. Tsoleridis, Panagiotis & Choudhury, Charisma F. & Hess, Stephane, 2022. "Deriving transport appraisal values from emerging revealed preference data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 225-245.
    9. 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).
    10. Basso, Franco & Núñez, Matías & Paredes-Belmar, German & Pezoa, Raúl & Varas, Mauricio, 2024. "Estimation of stops of last-mile delivery vehicles: An application in the food industry in the city of Santiago de Chile," Journal of Transport Geography, Elsevier, vol. 116(C).
    11. Liu Su & Changhyun Kwon, 2020. "Risk-Averse Network Design with Behavioral Conditional Value-at-Risk for Hazardous Materials Transportation," Transportation Science, INFORMS, vol. 54(1), pages 184-203, January.
    12. Hamzeh Alizadeh & Bilal Farooq & Catherine Morency & Nicolas Saunier, 2018. "On the role of bridges as anchor points in route choice modeling," Transportation, Springer, vol. 45(5), pages 1181-1206, September.
    13. Maoh, Hanna & Dimatulac, Terence & Khan, Shakil & Litwin, Marek, 2021. "Studying border crossing choice behavior of trucks moving between Ontario, Canada and the United States," Journal of Transport Geography, Elsevier, vol. 91(C).
    14. Siripirote, Treerapot & Sumalee, Agachai & Ho, H.W., 2020. "Statistical estimation of freight activity analytics from Global Positioning System data of trucks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).

    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. Carlo Prato & Shlomo Bekhor & Cristina Pronello, 2012. "Latent variables and route choice behavior," Transportation, Springer, vol. 39(2), pages 299-319, March.
    2. Kazagli, Evanthia & Bierlaire, Michel & Flötteröd, Gunnar, 2016. "Revisiting the route choice problem: A modeling framework based on mental representations," Journal of choice modelling, Elsevier, vol. 19(C), pages 1-23.
    3. Li, Dawei & Feng, Siqi & Song, Yuchen & Lai, Xinjun & Bekhor, Shlomo, 2023. "Asymmetric closed-form route choice models: Formulations and comparative applications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    4. S. F. A. Batista & Ludovic Leclercq, 2019. "Regional Dynamic Traffic Assignment Framework for Macroscopic Fundamental Diagram Multi-regions Models," Transportation Science, INFORMS, vol. 53(6), pages 1563-1590, November.
    5. Papola, Andrea & Tinessa, Fiore & Marzano, Vittorio, 2018. "Application of the Combination of Random Utility Models (CoRUM) to route choice," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 304-326.
    6. Evanthia Kazagli & Michel Bierlaire & Matthieu de Lapparent, 2020. "Operational route choice methodologies for practical applications," Transportation, Springer, vol. 47(1), pages 43-74, February.
    7. Hamzeh Alizadeh & Bilal Farooq & Catherine Morency & Nicolas Saunier, 2018. "On the role of bridges as anchor points in route choice modeling," Transportation, Springer, vol. 45(5), pages 1181-1206, September.
    8. Moshe Ben-Akiva & Song Gao & Lu Lu & Yang Wen, 2015. "DTA2012 Symposium: Combining Disaggregate Route Choice Estimation with Aggregate Calibration of a Dynamic Traffic Assignment Model," Networks and Spatial Economics, Springer, vol. 15(3), pages 559-581, September.
    9. Tomhave, Benjamin J. & Khani, Alireza, 2022. "Refined choice set generation and the investigation of multi-criteria transit route choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 484-500.
    10. Bekhor, Shlomo & Prato, Carlo Giacomo, 2009. "Methodological transferability in route choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 422-437, May.
    11. Hsueh, Chieh & Lin, Jen-Jia, 2023. "Influential factors of the route choices of scooter riders: A GPS-based data study," Journal of Transport Geography, Elsevier, vol. 113(C).
    12. Felipe González & Carlos Melo-Riquelme & Louis Grange, 2016. "A combined destination and route choice model for a bicycle sharing system," Transportation, Springer, vol. 43(3), pages 407-423, May.
    13. Scott, Darren M. & Lu, Wei & Brown, Matthew J., 2021. "Route choice of bike share users: Leveraging GPS data to derive choice sets," Journal of Transport Geography, Elsevier, vol. 90(C).
    14. Mai, Tien & Fosgerau, Mogens & Frejinger, Emma, 2015. "A nested recursive logit model for route choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 100-112.
    15. Tien Mai & Fabian Bastin & Emma Frejinger, 2018. "A decomposition method for estimating recursive logit based route choice models," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 253-275, September.
    16. Łukawska, Mirosława & Paulsen, Mads & Rasmussen, Thomas Kjær & Jensen, Anders Fjendbo & Nielsen, Otto Anker, 2023. "A joint bicycle route choice model for various cycling frequencies and trip distances based on a large crowdsourced GPS dataset," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    17. Dieter, Peter & Caron, Matthew & Schryen, Guido, 2023. "Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework," European Journal of Operational Research, Elsevier, vol. 311(1), pages 283-300.
    18. Rasmussen, Thomas Kjær & Watling, David Paul & Prato, Carlo Giacomo & Nielsen, Otto Anker, 2015. "Stochastic user equilibrium with equilibrated choice sets: Part II – Solving the restricted SUE for the logit family," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 146-165.
    19. Guo, Zhan & Wilson, Nigel H.M., 2011. "Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 91-104, February.
    20. Leite Mariante, Gabriel & Ma, Tai-Yu & Van Acker, Véronique, 2018. "Modeling discretionary activity location choice using detour factors and sampling of alternatives for mixed logit models," Journal of Transport Geography, Elsevier, vol. 72(C), pages 151-165.

    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:eee:transe:v:77:y:2015:i:c:p:29-44. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.