Modelling work- and non-work-based trip patterns during transition to lockdown period of COVID-19 pandemic in India

The impact of COVID-19 lockdown measures on gendered mobility patterns in France

The COVID-19 crisis has upset the way of life of our society. The objective of this study was to apprehend the consequences of public health policies on mobility through the lens of gender. The analyses are based on a representative sample of 3000 people living in France. Travel behaviour was quantified using three mobility indicators (number of daily trips, daily distance travelled and daily travel time) that we regressed on individual and contextual explanatory variables. Two periods were studied: lockdown (March 17, 2020 until May 11, 2020), and post-lockdown (a curfew period: January-February 2021). For the lockdown period, our results show: (i) a statistically significant gender difference for the three mobility indicators. On average, women made 1.19 daily trips versus 1.46 for men, travelled 12 km whereas versus 17 km for men and spent less time on travel (23 min) than men (30 min); (ii) the degree of mobility was particularly sensitive to access to a car, according to a gender difference. For the post-lockdown period, our results reveal that: (i) women were more likely than men to make a higher number of daily trips (OR = 1.10, 95% CI = [1.04-1.17]); (ii) having only one or no car in the household impacted the mobility of women during the post-lockdown period; (iii) women regained some mobility but without reaching the pre-lockdown level. A better understanding of the factors influencing mobility behaviour, in lockdown and curfew periods, can provide some pathways to improve transport planning and help public authorities while tackling gender inequalites.

Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea - a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators - from perspectives of trip frequency, spatial diversity, and travel range - to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors' mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors' transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.

Non-commuting intentions during COVID-19 in Nanjing, China: A hybrid latent class modeling approach

Non-commuting travel is essential for people to meet daily demands and regulate mental health, which is greatly disrupted due to the COVID-19 pandemic. To explore non-commuting intentions during COVID-19 across different groups of residents, this paper uses online survey data in Nanjing and constructs a hybrid latent class choice model that combines sociodemographic characteristics and psychological factors. Results showed that the respondents can be divided into two groups: the "cautious" group versus the "fearless" group. The "cautious" group with lower willingness to travel tend to be older, higher-income, higher-educated, female and full-time employees. Furthermore, the "cautious" group with higher perceived susceptibility is more obedient to government policies. In contrast, the "fearless" group is significantly affected by perceived severity and is more inclined to turn to personal protection against the pandemic. These results suggested that non-commuting trips were influenced not only by individual characteristics but also by psychological factors. Finally, the paper provides implications for the government to formulate COVID-19 management measures for the heterogeneity of different groups.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents

Introduction

This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment.

Methods

Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities.

Results

Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city.

Discussion

Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

How mentally healthy we are during the pandemic? Findings from changed travel behavior

BACKGROUND

Many people changed their travel behavior during the coronavirus pandemic with more telecommuting, fewer trip frequencies, and less use of transit and ride-hailing to avoid infection. The lack of outdoor activities may result in social isolation and then trigger anxiety or depressive symptoms.

RESEARCH OBJECTIVE

This study examines the relationship between anxiety and depression, and correlates various sociodemographic, income, job status, health-related factors, and travel behavior changes in six large U.S. cities.

DATA

U.S. Census Household Pulse Survey Phases 3.0 and 3.1 are employed.

METHOD

GAD-2 and PHQ-2 are used to screen the scores of anxiety and depression. The synthetic minority oversampling technique is applied to correct sample distribution. The multivariate mixed model is employed to examine relationships.

RESULTS

(1) Anxiety and depression are positively correlated, and the percentage of high anxiety is greater than the percentage of high depression. (2) The levels of anxiety and depression significantly vary across the six cities. (3) Women, young, singles, and white people have higher levels of anxiety and depression during the pandemic. (4) People who are willing to receive vaccination tend to have higher levels of anxiety and depression. (5) The prevalence of depressive disorders is significantly lower in the high-income group. (6) People who applied for unemployment insurance and experienced expense difficulties are more likely to suffer high levels of anxiety and depression. (7) Travel behavior changes, measured by increased telecommuting, reduced trip frequency, and reduced use of transit and ride-hailing, all suggest positive correlations with anxiety and depression.

CONCLUSIONS

More assistance and attention should be given to women, singles, and low-income households to reduce the prevalence of mental stress in vulnerable groups. Telecommuting can be but need to work with other travel demand management strategies. Travel and outdoor activities should be promoted under the new normal.

Passenger's perception about city buses in the aftermath of COVID 19: Experience from Guwahati city, India

The COVID-19 epidemic has affected public transport in various cities worldwide, including Guwahati city in India, where a 59.09% decline in the ridership of city bus services occurred in autumn 2021. The present work aims to investigate the impact of COVID-19 on travelers' perception and identify the parameters associated with the user's dissatisfaction for improving bus service in Guwahati city. In addition, the study also estimated the revenue loss of bus services due to this pandemic. This was carried out based on survey data from travelers in pre-covid (January 2019) situation, unlocked with 50% seating capacity (November 2020), and unlocked with 100% seat capacity plus some limited amount of standee (January 2021) situation. Results showed an increase in the number of users' priorities in post covid compared to pre covid conditions. Factors such as comfort, fairness, reliability & convenience, and safety are the priority areas of users' pre covid. Whereas bus service attribute such as vehicle condition & hygiene emerged as an additional priority of users post covid. Unfortunately, the bus service in the city performed poorly in terms of hygiene, leading to the shifting of the user to other modes of transport. The poor condition of the vehicles is another major factor that made the users to discard the bus service. This led to huge financial losses of the public transit authorities. The cost analysis result showed that the city bus operators faced a financial loss due to the COVID19 outbreak. The average wage of the drivers reduced from 780 Rs./day to 339 Rs./day in the first unlock phase and 476 Rs./day in the second unlock phase. The present study discusses the introduction of the new route in the city, regular cleaning, regular servicing of the buses, and other recommendations to improve the bus service in Guwahati city.

In-store or online grocery shopping before and during the COVID-19 pandemic

This paper presents results of a unique stated choice (SC) experiment to uncover the determinants of grocery shopping channel choice during the first wave of COVID-19 infections, where the most restrictive containment measures were in place. The choice sets were framed under regular and pandemic conditions, allowing for the estimation of pandemic-specific effects for each of the choice attributes. Our results show a significant overall increase of about 13%-points in online grocery shopping under pandemic conditions. Shopping and delivery costs were found to be the major decision drivers in both experimental settings, while the waiting time in front of the grocery store and risk of infection only played an secondary role. The value of delivery time savings (VDTS) decreases from about 10.8 CHF/day in the regular to 7.4 CHF/day in the pandemic case, indicating that respondents show an increased patience when waiting for the delivery of the ordered groceries. However, choice attributes related to the shopping trip, i.e. travel time and cost, do not show any notable effects. The COVID-19 death risk was valued rather low by the respondents and the relatively unrestricted Swiss containment measures are in line with the respondents' average preferences, as shown by a relatively low value of statistical life (VSL) of about 800,000 CHF.

Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities

By the end of 2021, the Omicron variant of coronavirus disease 2019 had become the dominant cause of a worldwide pandemic crisis. This demands a deeper analysis to support policy makers in creating interventions that not only protect people from the pandemic but also remedy its negative effects on the economy. Thus, this study investigated people's mobility changes through the relationship between spatiotemporal population density and urban facilities. Results showed that places related to daily services, restaurants, commercial areas, and offices experienced decreased visits, with the highest decline belonging to commercial facilities. Visits to health care and production facilities were stable on weekdays but increased on holidays. Educational institutions' visits decreased on weekdays but increased on holidays. People's visits to residential housing and open spaces increased, with the rise in residential housing visits being more substantial. The results also confirmed that policy interventions (e.g., declaration of emergency and upgrade of restriction level) have a great impact on people's mobility in the short term. The findings would seem to indicate that visit patterns at service and restaurant places decreased least during the pandemic. The analysis outcomes suggest that policy makers should pay more attention to risk perception enhancement as a long-term measure. Furthermore, the study clarified the population density of each facility type in a time series. Improving model performance would be promising for tracking and predicting the spread of future pandemics.

Insight into the nonlinear effect of COVID-19 on well-being in China: Commuting, a vital ingredient

BACKGROUND

COVID-19 had a devastating impact on people's work, travel, and well-being worldwide. As one of the first countries to be affected by the virus and develop relatively well-executed pandemic control, China has witnessed a significant shift in people's well-being and habits, related to both commuting and social interaction. In this context, what factors and the extent to which they contribute to well-being are worth exploring.

METHODS

Through a questionnaire survey within mainland China, 688 valid sheets were collected, capturing various aspects of individuals' life, including travel, and social status. Focusing on commuting and other factors, a Gradient Boosting Decision Tree (GBDT) model was developed based on 300 sheets reporting working trips, to analyze the effects on well-being. Two indicators, i.e., the Relative Importance (RI) and Partial Dependency Plot (PDP), were used to quantify and visualize the effects of the explanatory factors and the synergy among them.

RESULTS

Commuting characteristics are the most critical ingredients, followed by social interactions to explain subjective well-being. Commuting stress poses the most substantial effect. Less stressful commuting trips can solidly improve overall well-being. Better life satisfaction is linked with shorter confinement periods and increased restriction levels. Meanwhile, the switch from in-person to online social interactions had less impact on young people's life satisfaction. Older people were unsatisfied with this change, which had a significant negative impact on their life satisfaction.

CONCLUSIONS

From the synergy of commuting stress and commuting time on well-being, the effect of commuting time on well-being is mediated by commuting stress in the case of China. Even if one is satisfied with online communication, the extent of enhancement on well-being is minimal, for it still cannot replace face-to-face interaction. The findings can be beneficial in improving the overall well-being of society during the pandemic and after the virus has been eradicated.

COVID-19 impacts on mobility and travel demand

Since the beginning of the COVID-19 pandemic, many travel restriction policies were implemented to reduce further spread of the virus. These measures significantly affected travel demand to levels which could not have been anticipated by most planners in transportation agencies. As the pandemic has proven to have significant short-term impacts, it is anticipated that some of these impacts may translate to longer-term impacts on overall travel behavior and the movement of people and goods. Beyond the pandemic, the observed travel patterns during this period also provides a great opportunity for planners to assess policies such as work from home and remote learning as strategies to manage travel demand. This study provides a scenario analysis framework to re-evaluate travel demand forecasts under uncertain future conditions using the Maryland Statewide Transportation Model (MSTM). Model parameters associated with working from home, household income, changes in discretionary travel, distance learning, increased e-commerce, vehicle occupancy and mode choice were identified. Parameter values were assigned under the various scenarios using employer surveys on workforce teleworking and observed data on e-commerce growth and shopping behavior. The main findings of this study capture the sensitivities of systemwide vehicle miles travel, and vehicle hours travel under different scenarios and implications on future investment decisions. The study found that future investments under the scenarios remain beneficial to systemwide performance and therefore justified. Although this study focuses on the state of Maryland, the scenario framework and parameter definitions can be used in other states or agencies within a travel demand model environment.

Impact of pre-pandemic travel mobility patterns on the spatial diffusion of COVID-19 in South Korea

Introduction

Physical mobility is critical for the spread of infectious diseases in humans. However, few studies have conducted empirical investigations on the impact of pre-pandemic travel mobility patterns on the diffusion of coronavirus disease 2019 (COVID-19). Therefore, this study examines its impact at the city-county level on the diffusion by the wave period during the two-year pandemic in South Korea.

Methods

This study first employs factor analysis by using the travel origin-destination data by travel mode at the county level as of 2019 to derive pre-pandemic travel mobility patterns. Next, the study identifies how they had affected the diffusion of COVID-19 over time by employing the negative binomial regression models on confirmed COVID-19 cases for each wave, including the entire pandemic period.

Results

The study derived five pre-pandemic mobility patterns: 1) rail-oriented mobility, 2) intra-county bus-oriented mobility, 3) road-oriented mobility, 4) high-speed rail-oriented mobility, and 5) inter-county bus-oriented mobility. Among them, the biggest risk to the diffusion of COVID-19 was the rail-oriented mobility before the pandemic if controlling such measures as accessibility, physical environment, and demographic and socioeconomic indicators. In addition, the order of the magnitudes for the impact of pre-pandemic travel mobility factors on its spatial diffusion had not changed during experiencing the three different wave periods during the two-year pandemic in South Korea.

Conclusions

The study concludes that the rail-oriented travel mobility pattern before the pandemic could pose the greatest threat factor to the spatial spread of COVID-19 at any scale and time. Policymakers should develop strategies to prevent the spatial spread of COVID-19 by reducing human mobility for daily living in areas with strong rail mobility patterns formed before the pandemic.

Travel Behaviour of Vulnerable Social Groups: Pre, during, and Post COVID-19 Pandemic

Since the emergence of COVID-19, travel restrictions due to the pandemic have influenced several activities, in particular the mobility patterns of individuals. Our main goal is to draw the attention of scholars and policy makers to a specific segment of the population, namely (1) older people, (2) persons with disabilities (PwDs), (3) females, and (4) low-income population that are more vulnerable for travel behaviour change due to crisis such as the COVID-19 pandemic. This article systematically reviews the studies that have explored the implications of COVID-19 for the mobility and activities of individuals pre-, during, and post-pandemic using the PRISMA method. It is found that there are a few studies regarding the travel and mobility needs and challenges of older people and PwDs, and there is no direct study concerning female and low-income individuals while such crisis exist. Questions such as "What are the adverse impacts of restrictions on their travel behaviour?", "How can they travel safely to work, shopping, and medical centres?", "Which transportation modes can be more effective for them?", and "What are the government and policy makers' role in providing accessible and affordable mobility services in the presence of such crisis?" are without relevant answers in the literature.

Data analytics during pandemics: a transportation and location planning perspective

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

Potential long-term effects of Covid-19 on telecommuting and environment: An Italian case-study

To contain the sudden spread of SARS-CoV-2, many governments encouraged people to work from home, generating an unprecedented diffusion of this activity. Furthermore, Covid-19 has induced drastic changes in everyday life and travel habits, which might persist in the future. This paper aims to understand and estimate the potential long-term impacts of telework on the environment due to the pandemic, by analyzing factors affecting the frequency of telecommuting, the mode choice for traveling to work, and pollutant emissions generated by these trips. Data from a mobility survey administered in Padova (Italy) was used. Results indicate that Covid-19 could cause a rebound effect reversing the positive impacts of working from home, since, even if the number of trips could be reduced, many shifts towards non-sustainable travel modes could occur. The promotion of telework should be combined with measures fostering sustainable travel habits to pave the way towards a future green mobility.

Impacts of COVID-19 Travel Restriction Policies on the Traffic Quality of the National and Provincial Trunk Highway Network: A Case Study of Shaanxi Province

According to recent research, the COVID-19 pandemic has impacted road traffic quality. This study aims to analyze the impacts of COVID-19 travel restriction policies on the traffic quality of the national and provincial trunk highway network (NPTHN) in Shaanxi Province. We collected the traffic data of the NPTHN for three consecutive years (from 2019 to 2021), before and after the COVID-19 outbreak, including weekly average daily traffic, weekly traffic interruption times, weekly traffic control time, weekly traffic accidents, weekly traffic injuries, and weekly traffic deaths. Using descriptive statistics and dynamic analysis methods, we studied the safety and service levels of the NPTHN. We set up an assessment model of the NPTHN operational orderliness through dissipative structure theory and entropy theory to study the operational orderliness of the NPTHN. Results show that in 2020, the service level, safety level, and operational orderliness of the NPTHN dropped to the lowest levels. The pandemic was gradually brought under control, and the travel restriction policies were gradually reduced and lifted. The adverse impacts on the operational orderliness of the NPTHN decreased, but the operational orderliness did not yet recover to the pre-pandemic level. Meanwhile, the service and safety levels of the NPTHN did not recover. Taken together, the COVID-19 travel restriction policies had adverse impacts on the traffic quality of the NPTHN in Shaanxi Province.

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.

Consumer responses towards essential purchases during COVID-19 pan-India lockdown

Humanity experienced one of the worst crises in recent history due to the COVID-19 pandemic. The spread of the disease and the lockdown announced by the government of India created an emergency, disrupting the supply of essential commodities and creating panic and anxiety among the people. This paper aims at capturing the behavior of consumers purchasing essential commodities before and during the lockdown using an online questionnaire. Responses from 730 households covering 20 states in India were used. The data analysis revealed that consumers made a lesser number of trips during lockdown but purchased excess commodities considering the future uncertainties. The local family grocery stores, called kirana shops served well during the pandemic. During the lockdown, consumers made shorter trips by vehicles and walked extensively. Income was found to influence purchase behavior. The disruptions at the organized retail stores for in-store as well as online purchases were identified using factor analysis. Out of the three factors identified each for in-store and online purchases, perceived risk and vendor distrust had major influence respectively. The findings of this study give pointers to many infrastructure and policy initiatives that target tackling such emergencies in the future.

Association of human mobility with road crashes for pandemic-ready safer mobility: A New York City case study

BACKGROUND

The COVID-19 pandemic has reshaped our cities in many ways. The number of motor vehicles on the road has plummeted during lockdowns, and an increasing number of people are turning to walking and biking. From a road safety perspective, the overall question is what effects the human behavior shift brings on the crash occurrence and, more importantly, how to support decision-makers on safer mobility policies?

METHOD

Based on anonymous mobile phone location and crash report data in New York City, this study attempts to provide some new insights by using survival analysis (the hazard function approach) to explore the effects of human mobility changes due to the pandemic on crashes that involve injuries and fatalities (of pedestrian, cyclist or motorist).

RESULTS

(1) the increased percentage of people staying at home improves pedestrian and cyclist safety, which adds evidence for making walking and cycling more appealing; (2) the increased percentage of people staying at home raises the likelihood of injuries for motor vehicle drivers, suggesting that it will be critical to monitor the driving behavior and establish new speed limits during the future pandemic waves and in the post-pandemic era as well; (3) non-work trips (e.g., shopping, recreation, personal business, etc.) are positively associated with crash injuries for motor vehicle drivers as well as pedestrian and cyclist; (4) human mobility factors were found not related to crash fatalities; (5) control NPIs implemented increased the motor vehicle drivers' crash risk.

Impacts of the COVID-19 pandemic in the demand for urban transportation in Budapest

Social distancing guidelines established amid the COVID-19 pandemic have decreased the number of trips in urban transportation networks; furthermore, travelers have shifted away from high occupancy modes due to the fear of contagion. This scenario has led to reduced public transportation ridership and increased shares of private cars, cycling and walking in urban areas. In the international literature, predictive models for this scenario of changed travel behavior and imminent needs for operations and planning adjustments, however, are still scarce or limited in scope.

Holt-Winter’s multiplicative method was used to extrapolate pre-pandemic datasets as a means to evaluate the impacts of the pandemic in transportation activities in Budapest. Data from March 2020 indicate that stay-at-home orders have resulted in intra-city and commuter traffic reductions of about 35%, while public transportation ticket sales decreased by 90%. Bicycle traffic, on the other hand, increased by about 13% in the same period. These observations suggest that the COVID-19 pandemic has driven significant changes in trip generation and mode choice in Budapest.

This study proposes the adjustment of a pre-existing four-step transportation model of Budapest based on the introduction of contextual explanatory variables and on the recalibration of model parameters in order to reflect pandemic-related trends in trip generation and trip distribution. The recalibration and validation of the model were based on data from the first wave of the pandemic in Hungary. Validation results, although limited, suggest that the traditional four-step models are able to capture the impacts on transportation of the atypical scenario of a pandemic with relatively simple adjustments and few data requirements.

Modelling brake transition time of young alcohol-impaired drivers using hazard-based duration models

Braking performance of drivers is a crucial factor in evaluating the collision patterns and implementing road safety measures. Further, alcohol is known to impair driving control. The present study aims to examine the influence of a comprehensive range of Blood Alcohol Concentration (BAC) levels (0%, 0.03 %, 0.05 % and 0.08 %) on brake transition times of drivers. As young drivers show significantly higher crash risks compared to the experienced drivers, fifty-four young Indian drivers in the age group of 21-25 years (forty males and fourteen females) participated in the driving simulator experiments. The study adopted the framework of a within-subjects design, where each driver encountered rural and urban driving scenarios in a counterbalanced order, during experimental driving at each of the four BAC levels. Their brake transition times were estimated with respect to sudden pedestrian crossing events. Weibull Accelerated Failure Time (AFT) models with shared frailty were developed for quantifying the effects of BAC levels along with driver attributes on brake transition time. Preliminary analysis showed significant main effects of BAC (p < 0.001) and driving environment (p = 0.002) on brake transition time; however, their interaction effect was not significant (p = 0.485). The models revealed that 0.03 %, 0.05 % and 0.08 % BACs significantly reduced the brake transition times by 16 %, 28 % and 52 % in rural driving environment, and by 23 %, 37 % and 53 % in urban driving environment, compared to 0% BAC. The study outcomes may find application in assisting collision warning systems which take into account the braking behaviour of drivers.

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

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.