The Determinants of Walking Behavior before and during COVID-19 in Middle-East and North Africa: Evidence from Tabriz, Iran

Estimating Mode of Transport in Daily Mobility during the COVID-19 Pandemic Using a Multinomial Logistic Regression Model

At the beginning of 2020 there was a spinning point in the travel behavior of people around the world because of the pandemic and its consequences. This paper analyzes the specific behavior of travelers commuting to work or school during the COVID-19 pandemic based on a sample of 2000 respondents from two countries. We obtained data from an online survey, applying multinomial regression analysis. The results demonstrate the multinomial model with an accuracy of almost 70% that estimates the most used modes of transport (walking, public transport, car) based on independent variables. The respondents preferred the car as the most frequently used means of transport. However, commuters without car prefer public transport to walking. This prediction model could be a tool for planning and creating transport policy, especially in exceptional cases such as the limitation of public transport activities. Therefore, predicting travel behavior is essential for policymaking based on people's travel needs.

The Association between Active Mobility and Subjective Wellbeing during COVID-19 in MENA Countries

Objective: To augment the international scientific approach to raising public mental health through active lifestyle among adults, we added the evidence of the association between physical activity and subjective wellbeing in the Middle East and North Africa region by emphasizing the mediator—COVID-19. This study aimed to identify the correlations between active mobility and subjective wellbeing during the COVID-19 pandemic in a sample from Tabriz, Iran, which has not yet been tackled in previous study. Methods: We finalized an online survey (N = 603) from adults between 5 June and 15 July 2021. This group reported their individual and socio-economic characteristics and their perception features and location (e.g., home, work) during COVID-19. The paper developed three ordinal logistic regression (OLR) models to examine the association between active mobility types such as commute, non-commute, frequency of active travel to parks and services per week, and different subjective wellbeing including: 1- life satisfaction, 2- feeling energetic, and 3- peaceful mind while controlling for socio-economic variables (e.g., age, gender, education, job, and income) and objective commute distance. We also incorporated the most relevant objective (street length, land-use mix, number of intersections, and building and population density) and subjective (perceived distance to different services, perceived walking places, and perceived facilities attractiveness) factors. Results: Positive response relationships between four types of physical activity levels and subjective wellbeing scores were demonstrated in all of the three developed models (with significant levels of 0.05, and 0.1) with appropriate model fits, which confirmed the existing literature. However, these relationships showed different patterns (varied significant levels) for each type of subjective wellbeing. In addition, the factors including street length (p value: 0.004), perceived walkable places (p value: 0.021, 0.068, 0.017, and 0.025) (positively), population density, and perceived distance to shopping malls (p value: 0.076, <0.0001, and 0.059 (negatively) were associated with different indicators of subjective wellbeing. Conclusion: As for the implication of our study, special actions by urban authorities such as increasing mixed-use and creating attractive places will be required to enhance the walkability of the neighborhoods. Moreover, notifying the adults regarding the benefits of physical activity is much more needed.

Development of a Method for Evaluating Social Distancing Situations on Urban Streets during a Pandemic

In the New Normal era of “Living with COVID-19”, we need a measure of the safety of street spaces. Social distancing during a pandemic is considered an effective safety measure, but the current binary threshold approach to social distancing is clearly inadequate for evaluating and monitoring the risk of infection on urban streets. This study is to propose a social distancing indicator that can quantitatively evaluate the level of exposure to viral infection for pedestrians using urban streets during a pandemic, and to develop a statistical model to estimate the proposed indicator from simulations of pedestrian activity on urban streets. We assumed that the risk of infection on urban streets has a direct relationship with distance between pedestrians. The social distancing indicator was based largely on the findings of past studies. We developed a statistical model to relate the proposed indicator to three other explanatory variables: pedestrian density, clumpiness, and directional heterogeneity. We used pedestrian simulation to generate the raw data for these explanatory variables. The social distancing indicator demonstrated a statistically significant relationship with input variables and can be used to evaluate pedestrians’ social distancing on urban streets. We measured the relationship between different levels of pedestrian density, clumpiness, and directional heterogeneity and related the results to the potential level of exposure to viral infection. Health agencies can use the findings to develop appropriate policies for monitoring and improving the social distance between pedestrians on urban streets during a pandemic.