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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.

The coronavirus pandemic has affected everyday life to a significant degree. The transport sector is no exception, with mobility restrictions and social distancing affecting the operation of transport systems. This research attempts to examine the effect of the pandemic on the users of the public transport system of London through analyzing tweets before (2019) and during (2020) the outbreak. For the needs of the research, we initially assess the sentiment expressed by users using the SentiStrength tool. In total, almost 250,000 tweets were collected and analyzed, equally distributed between the two years. Afterward, by examining the word clouds of the tweets expressing negative sentiment and by applying the latent Dirichlet allocation method, we investigate the most prevalent topics in both analysis periods. Results indicate an increase in negative sentiment on dates when stricter restrictions against the pandemic were imposed. Furthermore, topic analysis results highlight that although users focused on the operational conditions of the public transport network during the pre-pandemic period, they tend to refer more to the effect of the pandemic on public transport during the outbreak. Additionally, according to correlations between ridership data and the frequency of pandemic-related terms, we found that during 2020, public transport demand was decreased while tweets with negative sentiment were being increased at the same time.