Public transport generally addresses the evident mobility needs and offers an often-irreplaceable service, especially for captive users and other disadvantaged population groups. Public transport design and services are closely related to the physical size of modern cities, the number of people living or working in them, and the distribution and organization of work and social activities. However, public transport has been restricted with the spread of the COVID-19 pandemic in Italy, since March 2020. Public transport demand collapsed, especially during the lockdown period (March-May 2020), and adverse effects were reported even in the subsequent periods. In fact, the social distancing restrictions have highlighted numerous problems with public transport systems worldwide, primarily due to two factors. The first is related to the spread of the virus via the respiratory route, which is more likely to infect in restricted areas, and the second is associated with a transport system that by definition has high occupancy rates and low spacing throughout the journey (e.g., the positioning of seats or standing places in a train or bus). Thus, the COVID-19 pandemic has substantially impacted the travel choices of users. The pandemic has also negatively affected the psychological state, generating specific problems of anxiety, fear, or stress among all population groups, even when choosing the means of transport to travel with.Given the emerging pandemic challenges, the present study examines the public transport demand characteristics during the various phases of the COVID-19 pandemic in Sicily, one of the most affected regions in Italy. The study investigates the mental state of a population sample that frequently used the local urban or regional public transport to travel to work before and during the pandemic phases in the Sicilian territory. Through the administration of an online survey, it was possible to collect sociodemographic and psychological data to understand the propensity to use public transport. A series of inferential statistical tests were applied to assess the correlation of psychological aspects (i.e., fear, anxiety, and stress) with socio-demographic variables and modal choice habits (trip frequency). Results highlight and evaluate each psychological issue among population groups and their relative role in shaping public transport-related preferences. The study highlights some proposals and their implementation strategies to prevent negative emotions and encourage public transport use in Sicily and generally.
This paper investigates how the travel behavior relating to Public Transport (PT) changed during the COVID-19 pandemic, and which are the expectations about the extent of PT use post-pandemic. A revealed preferences questionnaire survey was distributed within an academic community in the city of Thessaloniki, Greece. To understand the factors potentially determining the future PT use, hierarchical ordered probit and bivariate ordered probit models were estimated. Results showed that the frequent PT users reduced by almost 75% during the pandemic. More than 29% of the local academic community members are reluctant to resume PT use at pre-pandemic levels. Non-captive users, teleworkers and those being unsatisfied with cleanliness and safety are less willing to travelling by PT post-pandemic. Female and under-stress passengers were found to particularly appreciate the use of facemasks and the increased service frequencies as post-pandemic policy measures. The study findings can inform the recovery strategies of transport authorities in order to retain the PT ridership at levels that will not threat the long-term viability of service provision. Future research may complement these findings by examining other population segments, such as the commuters and the elderly, under more advanced modelling techniques to account for additional unobserved behavioral patterns.
The transportation network design and frequency setting problem concerns the optimization of transportation systems comprising fleets of vehicles serving a set amount of passengers on a predetermined network (e.g., public transport systems). It has been a persistent focus of the transportation planning community while, its NP-hard nature continues to present obstacles in designing efficient, all-encompassing solutions. In this paper, we present a new approach based on an alternating-objective genetic algorithm that aims to find Pareto optimality between user and operator costs. Extensive computational experiments are performed on Mandl’s benchmark test and prove that the results generated by our algorithm are 5–6% improved in comparison to previously published results for Pareto optimality objectives both in regard to user and operator costs. At the same time, the methods presented are computationally inexpensive and easily run on office equipment, thus minimizing the need for expensive server infrastructure and costs. Additionally, we identify a wide variance in the way that similar computational results are reported and, propose a novel way of reporting benchmark results that facilitates comparisons between methods and enables a taxonomy of heuristic approaches to be created. Thus, this paper aims to provide an efficient, easily applicable method for finding Pareto optimality in transportation networks while highlighting specific limitations of existing research both in regards to the methods used and the way they are communicated.
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.
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