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A Route Choice Model for the Investigation of Drivers’ Willingness to Choose a Flyover Motorway in Greece

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  • Ioannis Politis

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Georgios Georgiadis

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Aristomenis Kopsacheilis

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Anastasia Nikolaidou

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Chrysanthi Sfyri

    (Transport Engineering Laboratory, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Socrates Basbas

    (Department of Transportation & Hydraulic Engineering, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

The constant evolution of many urban areas ultimately reaches a point where the current infrastructure cannot further serve the needs of citizens. In the case of transport networks, congested roads, increased delay, and low level of service are among the indicators of a need for road infrastructure upgrade. Thessaloniki is the second-largest city in Greece with a population of over 1 million inhabitants in its metropolitan area. Currently, a significant share of the city’s traffic demand is served via its ring road, whose capacity is set to be enhanced through the construction of a flyover highway with the simultaneous upgrade of the existing ring road. The current study aims at investigating the key factors determining the final route choice of drivers between the two road axes. To that end, data from a combined revealed and stated preference survey targeting car drivers were collected, which were later exploited as the basis for the development of binary route choice regression and machine learning models. The results reveal that drivers’ choice is affected by criteria such as total travel time, the probability of accident occurrence, and closure time due to accident. The results of this paper could prove beneficial to transport researchers in forecasting drivers’ behavior in terms of route choice and to practitioners during the planning phase of similar infrastructure projects.

Suggested Citation

  • Ioannis Politis & Georgios Georgiadis & Aristomenis Kopsacheilis & Anastasia Nikolaidou & Chrysanthi Sfyri & Socrates Basbas, 2023. "A Route Choice Model for the Investigation of Drivers’ Willingness to Choose a Flyover Motorway in Greece," Sustainability, MDPI, vol. 15(5), pages 1-23, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4614-:d:1087965
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    References listed on IDEAS

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    1. Jou, Rong-Chang & Lam, Soi-Hoi & Liu, Yu-Hsin & Chen, Ke-Hong, 2005. "Route switching behavior on freeways with the provision of different types of real-time traffic information," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(5), pages 445-461, June.
    2. Ben-Elia, Eran & Shiftan, Yoram, 2010. "Which road do I take? A learning-based model of route-choice behavior with real-time information," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(4), pages 249-264, May.
    3. Zhao, Wenjing & Ma, Zhuanglin & Yang, Kui & Huang, Helai & Monsuur, Fredrik & Lee, Jaeyoung, 2020. "Impacts of variable message signs on en-route route choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 335-349.
    4. Junwei Zeng & Yongsheng Qian & Bingbing Wang & Tingjuan Wang & Xuting Wei, 2019. "The Impact of Traffic Crashes on Urban Network Traffic Flow," Sustainability, MDPI, vol. 11(14), pages 1-14, July.
    5. Siti Raudhatul Fadilah & Hiroaki Nishiuchi & An Minh Ngoc, 2022. "The Impact of Traffic Information Provision and Prevailing Policy on the Route Choice Behavior of Motorcycles Based on the Stated Preference Experiment: A Preliminary Study," Sustainability, MDPI, vol. 14(23), pages 1-21, November.
    6. Anders F. Jensen & Thomas K. Rasmussen & Carlo G. Prato, 2020. "A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    7. Shenhao Wang & Baichuan Mo & Stephane Hess & Jinhua Zhao, 2021. "Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark," Papers 2102.01130, arXiv.org.
    8. Srinivasan, Karthik K. & Mahmassani, Hani S., 2003. "Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: a dynamic kernel logit formulation," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 793-814, November.
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