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The usage of location based big data and trip planning services for the estimation of a long-distance travel demand model. Predicting the impacts of a new high speed rail corridor

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  • Llorca, Carlos
  • Ji, Joanna
  • Molloy, Joseph
  • Moeckel, Rolf

Abstract

Travel demand models are a useful tool to assess transportation projects. Within travel demand, long-distance trips represent a significant amount of the total vehicle-kilometers travelled, in contrast to commuting trips. Consequently, they pay a relevant role in the economic, social and environmental impacts of transportation. This paper describes the development of a microscopic long-distance travel demand model for the Province of Ontario (Canada) and analyzes the sensitivity to the implementation of a new high speed rail corridor.

Suggested Citation

  • Llorca, Carlos & Ji, Joanna & Molloy, Joseph & Moeckel, Rolf, 2018. "The usage of location based big data and trip planning services for the estimation of a long-distance travel demand model. Predicting the impacts of a new high speed rail corridor," Research in Transportation Economics, Elsevier, vol. 72(C), pages 27-36.
    • Handle: RePEc:eee:retrec:v:72:y:2018:i:c:p:27-36
    DOI: 10.1016/j.retrec.2018.06.004
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    References listed on IDEAS

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    5. Cartenì, Armando & Pariota, Luigi & Henke, Ilaria, 2017. "Hedonic value of high-speed rail services: Quantitative analysis of the students’ domestic tourist attractiveness of the main Italian cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 348-365.
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    Cited by:

    1. Shafida Azwina Mohd Shafie & Lee Vien Leong & Ahmad Farhan Mohd Sadullah, 2021. "A Trip Generation Model for a Petrol Station with a Convenience Store and a Fast-Food Restaurant," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    2. Borhan, Muhamad Nazri & Ibrahim, Ahmad Nazrul Hakimi & Miskeen, Manssour A. Abdulasalm, 2019. "Extending the theory of planned behaviour to predict the intention to take the new high-speed rail for intercity travel in Libya: Assessment of the influence of novelty seeking, trust and external inf," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 373-384.
    3. Deng, Taotao & Gan, Chen & Du, Huiping & Hu, Yukun & Wang, Dandan, 2021. "Do high speed rail configurations matter to tourist arrivals? Empirical evidence from China's prefecture-level cities," Research in Transportation Economics, Elsevier, vol. 90(C).
    4. Fan Yang & Fan Ding & Xu Qu & Bin Ran, 2019. "Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    5. Avogadro, Nicolò & Cattaneo, Mattia & Paleari, Stefano & Redondi, Renato, 2021. "Replacing short-medium haul intra-European flights with high-speed rail: Impact on CO2 emissions and regional accessibility," Transport Policy, Elsevier, vol. 114(C), pages 25-39.
    6. Fan Yang & Linchao Li & Fan Ding & Huachun Tan & Bin Ran, 2020. "A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    7. Mohsen Momenitabar & Zhila Dehdari Ebrahimi & Mohammad Arani, 2020. "A Systematic and Analytical Review of the Socioeconomic and Environmental Impact of the Deployed High-Speed Rail (HSR) Systems on the World," Papers 2003.04452, arXiv.org, revised Mar 2020.
    8. Van Acker, Veronique & Kessels, Roselinde & Palhazi Cuervo, Daniel & Lannoo, Steven & Witlox, Frank, 2020. "Preferences for long-distance coach transport: Evidence from a discrete choice experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 759-779.
    9. Mohsen Momenitabar & Raj Bridgelall & Zhila Dehdari Ebrahimi & Mohammad Arani, 2021. "Literature Review of Socioeconomic and Environmental Impacts of High-Speed Rail in the World," Sustainability, MDPI, vol. 13(21), pages 1-27, November.
    10. Bojan Jovanović & Kamer Shabanaj & Marko Ševrović, 2022. "Conceptual Model for Determining the Statistical Significance of Predictive Indicators for Bus Transit Demand Forecasting," Sustainability, MDPI, vol. 15(1), pages 1-18, December.

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    More about this item

    Keywords

    Travel demand model; Long-distance travel; High-speed rail; Location-based social network; Online trip planning;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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