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

Ioannis Politis, Georgios Georgiadis, Aristomenis Kopsacheilis, Anastasia Nikolaidou, Chrysanthi Sfyri and Socrates Basbas ()
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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

Sustainability, 2023, vol. 15, issue 5, 1-23

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.

Keywords: flyover; route choice model; stated preference; machine learning; ring road; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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