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

Temporal shifts in safety states through the COVID-19 pandemic: Insights from hidden semi-Markov models

The COVID-19 pandemic significantly impacted transportation safety, with an increase in risky driving behaviors observed during the initial lockdown period, leading to a higher likelihood of severe crashes. However, there is limited research on the post-pandemic effects on driving behaviors and safety. This study addresses this gap by analyzing open data from the state of Virginia to examine shifts in safety states from 2016 to 2024, covering the pre-, during-, and post-pandemic periods. Structural equation modeling (SEM) was utilized to measure latent variables representing aggressive and inattentive driving behaviors and to model their impacts on crash severity. Additionally, hidden semi-Markov models (HSMMs) were applied to infer shifts in safety states associated with these risky driving behaviors and the proportion of severe crashes. The strength of HSMM models lies in the ability to distinguish meaningful pattern changes from random noise. Compared with hidden Markov models (HMMs), HSMMs provide greater flexibility by accommodating arbitrary state duration distributions, contributing to better model performance and more reliable inferences. The HSMMs with four hidden states were utilized to reveal shifts in safety states over the eight-year analysis period in Virginia. Results suggested that safety states related to risky driving behaviors and the proportion of severe crashes were at lower-risk levels pre-pandemic from 2016 to 2019, then escalated to the highest-risk levels during the pandemic in 2020 and remained at higher-risk levels in 2021, 2022 and 2023. By 2024, safety states have returned to lower-risk levels similar to those inferred in the pre-pandemic period. A seasonal pattern was also identified in safety states, with lower-or-lowest-risk levels occurring in winter near the holiday season.

Air pollution prediction and backcasting through a combination of mobile monitoring and historical on-road traffic emission inventories

An important challenge for studies of air pollution and health effects is the derivation of historical exposures. These generally entail some form of backcasting, which refers to a range of approaches that aim to project a current surface into the past. Accurate backcasting is conditional upon the availability of historical data for predictor variables and the ability to capture spatial and temporal trends in these variables. This study proposes a method to backcast traffic-related air pollution surfaces developed using land-use regression models by including temporal variability of traffic and emissions and trends in concentrations measured at reference stations. Nitrogen dioxide (NO2) concentrations collected in the City of Toronto using the Urban Scanner mobile platform were adjusted for historical trends captured at reference stations. The Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST), a powerful tool for time series decomposition, was employed to isolate seasonal variations, annual trends, and abrupt changes in NO2 at reference stations, hence decomposing the signal. Exposure surfaces were generated for a period extending from 2006 to 2020, exhibiting decreases ranging from 10 to 50 % depending on the neighborhood, with an average of 20.46 % across the city. Yearly surfaces were intersected with mobility patterns of Torontonians extracted from travel survey data for 2006 and 2016, illustrating strong spatial gradients in the evolution of NO2 over time, with larger decreases along major roads and highways and in the central core. These findings demonstrate that air pollution improvements throughout the 14 years are inhomogeneous across space.

The impacts of Covid-19 pandemic on the sustainable mobility of university members in Turkey

The global Covid-19 pandemic had a devastating impact on our lives. The pandemic led to sudden and momentous changes in mobility styles and travel habits. Many users started preferring to travel via private vehicles, which is contrary to sustainability policies. Strict measures were implemented against the Covid-19 pandemic in Turkey during this process, as was the case all over the world. Taking into consideration these realities, the aim of the present study was to examine the impacts of the Covid-19 pandemic on the travel modes (public transportation, walking, and bicycle), anxiety and stress perceptions, and working conditions of individuals. A survey was conducted to measure the impacts of the pandemic and the measures taken. The sample of this study consists of people who regularly attended Suleyman Demirel University in Isparta and Akdeniz University in Antalya, Turkey, before the pandemic. An online survey was conducted for the case study during the May-June 2021 semester, and the survey was representative of the study population with a total of 556 participants. The travel time for both different university students decreased during the pandemic period. The percentage of those traveling to the university with their own cars increased to 77 % in AU and 66.8 % in SDU. The use of public transportation dropped to 6.1 % in AU and 11.8 % in SDU. 56.97 % of AU participants and 51.15 % of SDU participants reported that their walking habits decreased. It was reported that 52.73 % of the respondents in AU and 55.75 % of the respondents in SDU did not change their bicycle usage frequency. 64.24 % of AU and 74.42 % of SDU reported that their anxiety levels increased during the pandemic period. And for each of these analyses, there was no statistical difference between the two different university students.

How has COVID-19 changed private car use in European urban areas? An analysis of the effect of socio-economic characteristics and mobility habits

The private car has been identified as the main winner among transport modes in urban areas during the COVID-19 pandemic. The fear of contagion when using public transport or the decrease in road congestion are likely to have induced changes in citizens' travel habits with respect to cars. This work investigates the impact of the pandemic on individuals' habits and preferences regarding their car ownership levels and car usage in the European urban context, with a special focus on the role played by individual socio-demographics and urban mobility patterns. For this purpose, a Path Analysis approach has been adopted to model car ownership and use before and after COVID-19. The main data source employed in this research is an EU-Wide Urban Mobility Survey that collects detailed information (individual and household socio-economic characteristics, built environment attributes and mobility habits) of 10,152 individuals from a total of 21 European urban areas of different sizes, geographical locations, and urban forms. The survey data has been complemented with city-level variables that account for differences across the cities that may explain changes in car-related behaviour. The results show that the pandemic has induced an increase in car use among socio-economic groups that are generally associated with low car-dependent behaviour, revealing that policy instruments that discourage the use of the private car in urban areas are needed to avoid reversing past trends in the reduction of urban transport emissions. High-income, well-educated teleworkers are observed to be the ones that have reduced their car use to a larger extent. On the contrary, low-income individuals are mostly maintaining similar levels of car mobility. Finally, frequent public transport users are more likely than occasional users to have substituted this mode by the private car.

Impact of COVID-19 on traffic safety from the "Lockdown" to the "New Normal": A case study of Utah

During the past several years, the COVID-19 pandemic has had pronounced impacts on traffic safety. Existing studies found that the crash frequency was reduced and the severity level was increased during the earlier "Lockdown" period. However, there is a lack of studies investigating its impacts on traffic safety during the later stage of the pandemic. To bridge such a gap, this study selects Salt Lake County, Utah as the study area and employs statistical methods to investigate whether the impact of COVID-19 on traffic safety differs among different stages. Negative binomial models and binary logit models were utilized to study the effects of the pandemic on the crash frequency and severity respectively while accounting for the exposure, environmental, and human factors. Welch's t-test and Pairwise t-test are employed to investigate the possible indirect effect of the pandemic by influencing other non-pandemic-related factors in the statistical models. The results show that the crash frequency is significantly less than that of the pre-pandemic during the whole course of the pandemic. However, it significantly increases during the later stage due to the relaxed restrictions. Crash severity levels were increased during the earlier pandemic due to the increased traffic speed, the prevalence of DUI, reduced use of seat belts, and increased presence of commercial vehicles. It reduced to a level comparable to the pre-pandemic later, owing to the reduction of speed and increased seat-belt-used to the pre-pandemic level. As for the incoming "New Normal" stage, stakeholders may need to take actions to deter DUI and reduce commercial-vehicle-related crashes to improve traffic safety.

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

In this paper we investigate the public transport trip frequency variations, as well as the reasons that led to the shift away from public transport means, due to the COVID-19 pandemic. We studied relevant data from the Moovit platform, and we compared operational and trip frequency characteristics of public transport systems before and after the outbreak of the pandemic in 87 cities worldwide. On average, waiting times at public transport stops/stations increased while trip distances decreased, apparently due to the mobility restriction and social distancing measures implemented in 2020. Most of the Moovit users who said that they abandoned public transport in 2020 were found in Italy and Greece. We developed linear regression analysis models to investigate (among the 35 variables examined in the study) the relationship between public transport abandonment rates and socioeconomic factors, quality of service characteristics, and indicators of pandemic's spread. Empirical findings show that public transport dropout rates are positively correlated with the COVID-19 death toll figures, the cleanliness of public transport vehicles and facilities, as well as with the income inequality (GINI) index of the population, and thus reconfirm previous research findings. In addition, the waiting time at stops/stations and the number of transfers required for commute trips appeared to be the most critical public transport trip segments, which significantly determine the discontinuation of public transport use under pandemic circumstances. Our research findings indicate specific aspects of public transport services, which require tailored adjustments in order to recover ridership in the post-pandemic period.

Cabin aircraft comfort evaluation over high fidelity simulated flight

The primary purpose of this paper is to investigate the possibility of using a Full Flight Simulator (FFS) as an experimental setup for passengers' comfort analysis. Results based on subjective measurements are thus presented to assess comfort levels experienced during a simulated flight. A preliminary investigation has been conducted on a sample of 125 candidates to gain insight into the elements influencing the comfort level perceived based on the participants' actual flight experience; this suggested that the seat configuration is of great importance. Then, the experiment carried out by means of the FFS have been conducted on a reduced sample of 20 candidates for economic and organizational reasons. The behaviour of the 65% of the candidates has been analysed in a seating configuration comparable to the seat of a business-class aircraft. While the experience of the remaining 35% has been studied in an economy-type seat arrangement. Although the main variable under consideration was the seat, several environmental parameters were also considered during the experimental tests to evaluate their effects on perceived comfort level. During each simulated flight, passengers have been subjected to different levels of light intensity, noise, temperature and vibration associated with the different flight phases. Subjective data were collected using a questionnaire concerning every parameter and submitted to the passengers for each flight phase. The aim of varying the environmental parameters inside the cabin was to look for a relation between the subjective comfort level and each comfort parameter. In addition to perceived comfort based on the questionnaire, statistical analysis with parametric and non parametric tests revealed significant effects of environmental variables.

COVID-19, mobility restriction, and sexual behavior among a cohort of people of reproductive age: Nigeria

Background

One of the non-pharmaceutical strategies adopted by various governments to control the spread of COVID-19 is mobility restriction (MR), popularly known as a lockdown. Evidence shows that MR has some unintended consequences, such as increased cases of domestic violence, rape, pornography, sex chats, incest, and other unhealthy sexual behaviors (SBs).

Methods

The study examined the influence of MR on SB in Owerri via a cross-sectional quantitative approach. A total of 425 interviewees were randomly chosen among people of a reproductive age. Data were analyzed using univariate, bivariate, and multivariate levels of analysis.

Results

A significant relationship exists between selected socio-demographic characteristics, such as age and marital status, and the ability of people to cope with sexual abstinence. Results from the logistic regression analysis further illustrated this observation as during MR people were twice as likely to engage in prolific sex chats that could spur other harmful SBs.

Conclusion

It is recommended that people should be allowed to determine whether they would like to stay with their partners in subsequent lockdowns, or otherwise, to prevent some of the unpleasant SBs recorded.

Car Ownership and Post-COVID Urban Mobility across European Cities

The COVID-19 pandemic has greatly affected households’ mobility habits and choices in the past two years. Scarce evidence is available concerning medium term decisions such as the purchase or sale of a private vehicle. This work aims to explore the factors that have influenced households’ decisions to buy or sell a private car due to the pandemic and foresee how these decisions might influence their travel behaviour in the next few years. To achieve this goal, an EU-wide survey gathering information from 21 European cities was carried out during the second term of 2021, when lockdowns were no longer in place, but some mobility and sanitary restrictions were still maintained. The survey focuses on the impact of the pandemic and the consequent mobility restrictions on the transport habits and preferences of the respondents. The questionnaire collected socio-economic characteristics and mobility habits of 10,152 individuals. Interestingly, the analysis of the survey does not provide any evidence of a reduction of motorization rates for teleworkers. On the contrary, both partial and full-time teleworkers have bought significantly more cars than the rest of the groups. These findings greatly contrast with the fact that changes in daily habits resulting from the adoption of teleworking regimes are the main cause of reductions in car use among car buyers. This result suggests that further incentives for car ownership reduction are needed to harness the full potential of teleworking for decreasing car dependency. A positive relation between number of cars owned before COVID and car purchases has been found, pointing out that these purchases are probably not solving accessibility limitations for car-less households. Finally, a positive relation is also observed between car purchases and increased use of shared mobility services.

Evolution of teleworking and urban mobility changes driven by the COVID-19 pandemic across European Cities

Work-related travel is one of the primary travel purposes for citizens (Eurostat, 2021). The COVID-19 pandemic triggered significant changes in lifestyles and mobility that may persist in the long term. Teleworking and online shopping are much more widely adopted than before the pandemic crisis, while active mobility modes have gained a considerable share of urban transport activity. Restrictions in response to the COVID-19 pandemic have shown that several activities developed within the city can be done by ICT tools, such as working from home or shopping online, among others. Those tools can be used to avoid -potentially- unnecessary trips and consequently reduce Green House Gas (GHG) emissions. This work aims to determine how the COVID-19 pandemic affected the adoption of teleworking and how mobility behaviour has changed during the pandemic. It also explores the factors that may affect mobility changes, their long-term effects and potential repercussion on GHG emissions.

To do so, we use an extensive survey carried out in 20 European cities across 11 Member States, applying statistical inference analysis among different categories of variables focusing on active workers. Results show that teleworking has grown significantly during the pandemic, both in the number of users and in the frequency of use. The change in mobility patterns has been significant and might serve as a lever for change towards a more sustainable mobility, but also for the worse if the right decisions are not made consequently.

Challenges for urban transport policy after the Covid-19 pandemic: Main findings from a survey in 20 European cities

This article explores the changes in transport and mobility patterns that were triggered by the Covid-19 pandemic, as well as citizens' potential new expectations arising from this situation regarding public transport policy and urban planning. We conducted an extensive survey in 20 cities across 11 European countries during the second quarter of 2021, with a total of 10000 respondents. The information that we collected covered the change in the frequency of technology-based alternatives to travel -such as teleworking and online shopping-compared to the pre-pandemic period. It also included the change in trip frequency, purpose, mode and destination. Using the survey dataset as input, we applied a classification model that explains the change in mode choice based on the respondent characteristics and preferences. Our findings suggest that the changes in mobility patterns induced by the pandemic are the result of the combination of the degree of mobility restrictions imposed in each city, the adoption of new lifestyle and work patterns, and individual choices that depend on personal risk aversion. We identify three main challenges for urban transport policy: the increasing use of car that can reinforce long-term car dependency, the avoidance of public transport that may lead to financial and operational instability, and the uncertainty as regards the future of innovation in urban transport, especially as regards emerging technologies and business models.

The Anticipated Use of Public Transport in the Post-Pandemic Era: Insights from an Academic Community in Thessaloniki, Greece

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 impact of COVID-19 on future public transport use in Scotland

This paper examines the determinants of changes in future public transport use in Scotland after the COVID-19 pandemic. An online questionnaire was distributed to 994 Scottish residents in order to identify travel habits, attitudes and preferences during the different phases of the COVID-19 outbreak and travel intentions after the pandemic. Quota constraints were enforced for age, gender and household income to ensure the sample was representative of the Scottish population. The respondents indicated that they anticipated they would make less use of buses and trains at the end of the pandemic. Over a third expect to use buses (36%) and trains (34%) less, whilst a quarter expect to drive their cars more. As part of the analysis, a random parameter bivariate probit model with heterogeneity in the means of random parameters was estimated to provide insights into the socio-demographic, behavioural and perceptual factors which might affect future public transport usage. The inclusion of random parameters allows for the potential effects of unobserved heterogeneity within the independent variables to be captured, whilst making allowances for heterogeneity in the means of the random parameters. The model estimation showed that several factors, including pre-lockdown travel choices, perceived risk of COVID-19 infection, household size and region significantly affected intended future use of public transport. In addition, several variables related to age, region, pre-lockdown travel choices and employment status resulted in random parameters. The current paper contributes to our understanding of the potential loss of demand for public transport and the consequences for future equitable and sustainable mobility. Our findings are highly relevant for transport policy when developing measures to strengthen the resilience of the public transport system during and after the pandemic.

The impact of the COVID-19 pandemic on internal migration in Germany: A descriptive analysis

The COVID-19 pandemic has affected economies, labor markets, health care, education and tourism around the globe in unprecedented ways. However, little research has yet been devoted to the impact that the pandemic might have had on internal migration. This study aims to address this gap by determining how the intensity and spatial patterns of internal migration changed between 2019 and 2020 in Germany. We draw on data from the population register on annual flows between 401 counties. We find that the COVID-19 pandemic was associated with a 5% drop in the intensity of inter-county migration in 2020 compared to the previous year, with significant variation across age. The pandemic was also associated with an upsurge in net migration losses for the largest cities, driven by fewer inflows of young adults and continuing outflows of families.

New Habits of Travellers Deriving from COVID-19 Pandemic: A Survey in Ports and Airports of the Adriatic Region

The aim of this paper is to analyse the variations in the habits and the modes of transport of travellers departing from airports and ports during the COVID-19 outbreak. In the second year of the pandemic (i.e., from August to October 2021), travellers were invited to take part in an anonymous online survey at the terminal buildings of nine Italian and Croatian airports and ports located in the Adriatic region. Around 73% of respondents used public transport when travelling in the pre-COVID-19 period, whereas the corresponding share of respondents in the COVID-19 period was less than 50% and approximately 56% in the post-COVID-19 future. The main reason for not travelling by public transport was related to personal or sanitary reasons in the time of COVID-19. During the pandemic, around 39% of travellers preferred their own vehicle to public transport for moving to/from the airports and ports because of safety and sanitary reasons. With the pandemic, health was the main reason behind daily choices for up to 49% of the respondents. Moreover, a similar share of travellers considered health when choosing the transportation mode during and after the pandemic.

COVID-19 effects on urban driving, walking, and transit usage trends: Evidence from Indian metropolitan cities

The outbreak of the COVID-19 pandemic disrupted all walks of life, including the transportation sector. Fear of the contagion coupled with government regulations to restrict mobility altered the travel behavior of the public. This study proposes integrating freely accessible aggregate mobility datasets published by tech giants Apple and Google, which opens a broader avenue for mobility research in the light of difficult data collection circumstances. A comparative analysis of the changes in usage of different mobility modes during the national lockdown and unlock policy periods across 6 Indian cities (Bangalore, Chennai, Delhi, Hyderabad, Mumbai, and Pune) explain the spatio-temporal differences in mode usages. The study shows a preference for individual travel modes (walking and driving) over public transit. Comparisons with pre-pandemic mode shares present evidence of inertia in the choice of travel modes. Association investigations through generalized linear mixed-effects models identify income, vehicle registrations, and employment rates at the city level to significantly impact the community mobility trends. The methods and interpretations from this study benefit government, planners, and researchers to boost informed policymaking and implementation during a future emergency demanding mobility regulations in the high-density urban conglomerations.

Regional mobility during the Covid-19 pandemic: Analysis of trends and repercussions using mobile phones data across the EU

The analysis of mobile phones data at regional level in the EU reveals varying patterns in mobility trends during the Covid-19 pandemic. These depend on the temporal evolution of the pandemic in each EU Member State, the measures taken at local or national level to limit the growth of the pandemic, as well as the level of urbanization and type of economic activity in each region. During the first phase of the pandemic (March- April 2020) the decrease in mobility was in general uniform among regions in the same Member State, especially in Italy, Spain and France, where national level measures were adopted. A relaxation of the measures and a resulting rebound of mobility was evident during the summer period (July- August 2020). At the same time, a shift from urban to rural areas during the summer vacation period is evident, with especially touristic areas increasing the number of movements in the same Member State. The variance in mobility trends during the second wave of the pandemic (October- November 2020) was higher, a result of the predominantly local and regional level measures applied in each Member State. Those insights suggest a certain correlation between the level of mobility and the evolution of the pandemic at regional level. The association with high levels of Covid-19 prevalence is particularly strong in urban regions with high mobility levels.

Transport Systems for Sustainability: Policy, Planning and Exploitation

Sharing a common global vision for the improvement of the world, accompanied by a framework that operationalizes this vision, is a crucial step for the long-term sustainability of the planet and its people [...]

Active mobility versus motorized transport? User choices and benefits for the society

Measures promoting active mobility - walking or cycling - are often seen as an effective strategy to meet multiple urban objectives. The advantages of such behavioural changes cover multiple dimensions at public and individual level, including positive impacts on health, safety, climate, economy, environment and air quality. However, there is still a considerable potential for increasing the uptake of active mobility in urban areas. This paper explores the determinants of active mobility choice and compares the demographic, socio-economic and cultural factors that influence it. The methodology combines extensive survey data, an EU-wide transport model and detailed indicators of external costs of transport with a Gradient Boosting Machine Learning approach. The model based scenarios quantify the benefit in terms of external costs savings from increasing active mobility shares. Such savings - at EU level, can reach the amount of 15 billion euro per year for a shift of 10% of trips to active mobility modes.

Research and Innovation Supporting the European Sustainable and Smart Mobility Strategy: A Technology Perspective from Recent European Union Projects

Many concepts and innovations aim to improve transport and mobility, while helping to decrease the externalities that transport imposes on society. Research and innovation monitoring tools are important to assess the current state of development so that research funding and policy making efforts can be aligned optimally. This paper presents a comprehensive approach which links technological developments in the transport sector in Europe to the objectives of the most recent policy developments, in particular, the 2020 European Sustainable and Smart Mobility Strategy. It does so by identifying and evaluating technologies from European Union-funded projects between 2007 and 2020, by means of a technology taxonomy. Information is provided at an aggregated level on funding characteristics of both projects and the technologies, while at the same time, the level of maturity of researched technologies in the most recent projects is identified. This study can aid policy makers to support the future development of transport technologies as part of pertinent policy strategies and identify research gaps.

Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA

By using text mining techniques, this study identifies the topics of sustainable consumption that are important during the COVID-19 pandemic. An Application Programming Interface (API) streaming method was used to extract the data from Twitter. A total of 14,591 tweets were collected using Twitter streaming API. However, after data cleaning, 13,635 tweets were considered for analysis. The objectives of the study are to identify (1) the topics users tweet about sustainable consumption and (2) to detect the emotion-based sentiments in the tweets. The study used Latent Dirichlet Allocation (LDA) algorithm for topic modeling and the Louvain algorithm for semantic network clustering. NRC emotion lexicon was used for sentiment analysis. The LDA model discovers six topics: organic food consumption, food waste, vegan food, sustainable tourism, sustainable transport, and sustainable energy consumption. While the Louvain algorithm detects four clusters—lifestyle and climate change, responsible consumption, energy consumption, and renewable energy, sentiment analysis results show more positive emotions among the users than the negative ones. The study contributes to existing literature by providing a fresh perspective on various interconnected topics of sustainable consumption that bring global consumption to a sustainable level.

Mobility Trends before and after the Pandemic Outbreak: Analyzing the Metropolitan Area of Barcelona through the Lens of Equality and Sustainability

The spatial arrangement of a metropolis is of utmost importance to carry out daily activities, which are constrained by space and time. Accessibility is not only shaped by the spatial and temporal dimension, but it is also defined by individual characteristics, such as gender, impairments, or socioeconomic characteristics of the citizens living or commuting in this area. This study analyzes mobility trends and patterns in the metropolitan area of Barcelona before and after the COVID-19 pandemic outbreak, with special emphasis on gender and equality. The study draws on multiple sources of information; however, two main datasets are analyzed: two traditional travel surveys from the transport metropolitan area of Barcelona and two coming from smartphone data. The results show that gender plays a relevant role when analyzing mobility patterns, as already highlighted in other studies, but, after the pandemic outbreak, some population groups were more likely to change their mobility patterns, for example, highly educated population groups and those with higher income. This study also highlights that e-activities may shape new mobility patterns and living conditions for some population segments, but some activities cannot be replaced by IT technologies. For all these reasons, city and transport planning should foster sustainable development policies, which will provide the maximum benefit for society.

Teleworking and Online Shopping: Socio-Economic Factors Affecting Their Impact on Transport Demand

Teleworking and online shopping became commonplace during the COVID-19 pandemic and can be expected to maintain a strong presence in the foreseeable future. They can lead to significant changes in mobility patterns and transport demand. It is still unclear, however, how extensive their adoption can be, since each individual has different preferences or constraints. The overall impact on transport depends on which segments of the population will modify their behaviour and on what the substitutes to the current patterns will be. The purpose of this work is to identify the user profiles and spatial aspects that affect the adoption of teleworking and online shopping, and to explore the potential impact on transport demand. To that end, data from an EU-wide survey on mobility were analysed using a Machine Learning methodology. The results suggest that while the take up of the new work and consumption patterns is high on average, there are significant differences among countries and across different socio-economic profiles. Teleworking appears to have a high potential mainly in certain services sectors, affecting commuting patterns predominantly in large urban areas. Online shopping activity is more uniform across the population, although differences among countries and age groups may still be relevant. The findings of this work can be useful for the analysis of policies to encourage the uptake of new technologies in transport and mobility. They can be also a good reference point for future studies on the ex-post analysis of the impacts of the pandemic on mobility.