The expectations for post-COVID recovery of transport activity point towards a gradual return to normality, once the pandemic is under control and mobility restrictions end. The shock to society and economy has, however, caused a number of behavioural changes that can influence the evolution of the transport sector. We analyse the main factors that can influence future supply and demand and explore how they may affect trip generation, distribution and modal split in passenger transport. We combine several conventional and innovative data sources with a detailed strategic transport model at the EU level, in order to present quantitative estimates under various scenarios. New remote work patterns or personal risk avoidance attitudes can lead to increased levels of car ownership and use. Public policy priorities in the aftermath of the pandemic would need to address the emerging challenges and adopt measures that can sustain the shift to active travel, support public transport, railways and aviation and stimulate innovation in transport technologies and services.

https://www.mdpi.com/2071-1050/13/11/6359/pdf

The Post-Pandemic Recovery of Transport Activity: Emerging Mobility Patterns and Repercussions on Future Evolution

Behavioural changes in transport and future repercussions of the COVID-19 outbreak in Spain

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.

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A region-wide survey on emotional and psychological impacts of COVID-19 on public transport choices in Sicily, Italy

Aims Since active mobility is essential for older adults’ health and well-being, especially in crises, this study aims to investigate and compare factors affecting active mode choice of older adults (AMCOA) in the pre- and post-COVID-19 outbreak and evaluate changes in their active mobility behavior and no research has been conducted this subject. Methodology The sample study of this cross-sectional study includes individuals over 60 years old residing in Isfahan, Iran. For interviews based on a structured questionnaire, which was drafted based on reviewing the literature, 453 participants were randomly selected in 15 municipality districts of Isfahan. A binary logistic regression model was used to analyze the data. Result and Discussion The results indicate that in the post-outbreak the average walking duration per week decreased from 59 to 29 min; while, the share of this mode has increased from 40% to 65%. Also, the share of bicycles and the average cycling duration per week increased from 9% to 18% and from 9 to 15 min, respectively. Moreover, trip frequency, bicycle ownership, quality of walking and cycling routes, intersections safety, neighborhood security and greenery, traffic calming, CBD accommodation, and public transportation accessibility have positive effects on AMCOA; while, trip distance and vehicle ownership affect negatively. The results reveal that older adults have turned to the bicycle for most of their long trips during the pandemic because it is not subjected to traffic restrictions. Besides, the findings show that increasing bicycle ownership and improving bike-sharing infrastructure make the bicycle a resilient alternative when public transportation and private vehicles are not efficient. Conclusion Policymakers and urban planners should consider that an elderly-friendly neighborhood with mixed, dense, and accessible land uses and services, as well as safe and secure routes can increase older adults’ active mobility in the crises. Aims Since active mobility is essential for older adults’ health and well-being, especially in crises, this study aims to investigate and compare factors affecting active mode choice of older adults (AMCOA) before and during the first wave of COVID-19 outbreak and evaluate changes in their active mobility behavior and no research has been conducted this subject.

The impacts of COVID-19 on older adults' active transportation mode usage in Isfahan, Iran

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees; furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

https://www.mdpi.com/2071-1050/13/12/6596/pdf

Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

In this paper, we investigate the travel behavior changes in Thessaloniki, Greece aiming to understand them and explore the factors that affect them under the COVID-19 mobility restriction measures. Socioeconomic and mobility data from two questionnaire surveys, one year before and during the COVID-19 lockdown of April 2020 (with 1462 and 196 responses respectively), were compared by utilizing a wide variety of inductive statistical tests. Ordinary Least-Squares regression models and Cox proportional hazards duration models were employed to explore any concurrent socioeconomic effect on travel behavior patterns. Results showed that the number of daily trips per person was on average decreased by 50% during the lockdown. This decrease was much greater for the non-commuting trips. Trips on foot were increased, private car was mainly used for commuting and public transport modal shares were heavily reduced. Trip durations were generally increased, as travelling was considered a recreational activity per se. The starting times of the first trips of the day were more evenly distributed throughout the day and many travelers only started their first trips late in the afternoon. Older travelers generally maintained their mobility behavior patterns despite their higher vulnerability to COVID-19 disease. Lower-income travelers were likely to make more daily trips. Male travelers tended to make higher-duration trips compared to their female counterparts. Since pandemics may become recurring events in the future, our findings provide for a better understanding of their influence on mobility and support the design of customized policies to fulfill sustainable mobility objectives during lockdown circumstances.

COVID-19 lockdown measures and travel behavior: The case of Thessaloniki, Greece

COVID-19 pandemic is a challenge that the world had never encountered in the last 100 years. In order to mitigate its negative effects, governments worldwide took action by prohibiting at first certain activities and in some cases by a countrywide lockdown. Greece was among the countries that were struck by the pandemic. Governmental authorities took action in limiting the spread of the pandemic through a series of countermeasures, which built up to a countrywide lockdown that lasted 42 days. This research aims at identifying the effect of certain socioeconomic factors on the travel behaviour of Greek citizens and at investigating whether any social groups were comparatively less privileged or suffered more from the lockdown. To this end, a dynamic online questionnaire survey on mobility characteristics was designed and distributed to Greek citizens during the lockdown period, which resulted in 1,259 valid responses. Collected data were analysed through descriptive and inferential statistical tests, in order to identify mobility patterns and correlations with certain socioeconomic characteristics. Additionally, a Generalised Linear Model (GLM) was developed in order to examine the potential influence of socioeconomic characteristics to trip frequency before and during the lockdown period. Outcomes indicate a decisive decrease in trip frequencies due to the lockdown. Furthermore, the model’s results indicate significant correlations between gender, income and trip frequencies during the lockdown, something that is not evident in the pre-pandemic era.

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Mapping travel behavior changes during the COVID-19 lock-down: a socioeconomic analysis in Greece

A congested intersection is one of the major contributors of traffic delay, air and noise pollution in an urban area. A possible redesign of such an intersection can considerably reduce the above mentioned impacts and therefore achieve environmental and economic profits. The signalized intersection, under examination is one of the most congested in the city of Thessaloniki, located in northern Greece. Within the framework of this study, alternative design infrastructure schemes are proposed and examined through the use of microsimulation techniques. Each scenario consists of the construction of an underground bypass as well as a set of various traffic management interventions. In phase A of the study a full horizontal and vertical alignment study was conducted in order to secure the applicability of the proposed scenarios. In the second phase, the alternative scenarios are evaluated through microsimulation techniques and the software platform AIMSUN was used for traffic simulation. In order to model and calibrate the existing situation, a video of the morning peak hour was recorded followed by additional in-situ measurements. The main traffic indicators that were taken into account for calibration and validation were: the GEH index, the mean section queue length per 15min intervals, the traffic volume per 15 min intervals and the average travel time. In the final phase of the study, the proposed interventions were evaluated by comparing the profits from delay time and air pollutant emissions, with their total cost. Additionally, sustainability is evaluated through a survey targeted on postgraduate students, researchers and professionals.

Small scale intervention in a major city center interchange. Economic, environmental and sustainability analysis

https://doi.org/10.1016/j.cities.2023.104206

Factors affecting public transport performance due to the COVID-19 outbreak: A worldwide analysis

The improvement of road safety is considered as a top priority on the agendas of governments and transport policy making stakeholders worldwide, with the ‘Vision zero’ target being the pinnacle of the European Commission’s road safety strategy. Increased attention has been given to pedestrians, since crashes involving this vulnerable user group, have a higher mortality rate. As a result, research focusing on the behaviour of pedestrians and on the application of Intelligent Transport Systems that will assist pedestrians, is of increased importance. This paper attempts to predict pedestrian behaviour on crossings with a Countdown Signal Timer (CST), through the application of two machine learning algorithms. In the frame of the case study presented, an intersection in the Kalamaria city of Thessaloniki, Greece, where countdown signal timers have been installed on the pedestrian traffic lights, is analysed. For the needs of the analysis two models were implemented, a Deep Neural Network (DNN) and a Logistic Regression model. Results of both models indicated a satisfactory performance. In detail, the DNN model managed to estimate the pedestrians’ crossing speed with a Mean Squared Error value of 0.0647 (km/h) 2 , while the Logistic Regression model, which classified pedestrians based on their behaviour, achieved an accuracy of 97%. The accurate determination of pedestrians’ crossing behaviour could not only underline the influence of countdown signal timers, but also highlight appropriate countermeasures that can make infrastructure safer for this user group. Findings of this research can prove beneficial for researchers, infrastructure operators and policy makers alike, in their effort to improve the safety level of pedestrians. 

Aristomenis Kopsacheilis

Papers

COVID-19 lockdown measures and travel behavior: The case of Thessaloniki, Greece

Mapping travel behavior changes during the COVID-19 lock-down: a socioeconomic analysis in Greece

Small scale intervention in a major city center interchange. Economic, environmental and sustainability analysis

Factors affecting public transport performance due to the COVID-19 outbreak: A worldwide analysis

Applying deep learning techniques for the prediction of pedestrian behaviour on crossings with countdown signal timers.