This study attempted to investigate the impacts of COVID-19 on public transport usage in a hypothetical ‘new normal’ situation in a South Asian country, Bangladesh, by using data collected during the first wave of COVID-19. Most of the samples came from young and affluent groups. Findings of the study showed that a substantial proportion of respondents expected to reduce travel by public transport during the ‘new normal’ situation than the pre-pandemic situation. To identify the factors behind the expectation, a multinomial logistic regression model was developed. Results suggest that income, regular travel mode, frequency of travel by public transport in the pre-COVID-19 situation, expected change in trip frequency and virtual activities, risk perception, and trust in preventive strategies can influence public transport use during the ‘new normal’ situation. The results of the study would be useful in understanding the immediate impact of a pandemic on public transportation and help prepare better for future pandemics.

Although a large number of studies have tried to explore the relationship between built environment and pedestrian crash severity in developed countries, there is a lack of similar studies in the context of developing countries. Methodologically, the contributory factors influencing pedestrian crash severity are commonly identified through global logistic regression (GLR) models. However, these models are unable to capture the spatial variation in the relationships between the dependent and independent variables. The local logistic regression model, such as geographically weighted logistic regression (GWLR), can potentially overcome this issue. The application of local logistic regression to model pedestrian crash severity is absent in the literature. Therefore, this study aimed to apply the GWLR technique to explore spatially heterogeneous relationships between natural and built environment-related factors and pedestrian crash severity in Dhaka, the capital city of a developing country: Bangladesh. First, using secondary pedestrian crash data, a GLR model was developed to identify significant contributory factors influencing pedestrian crash severity. Results of the model showed that the probability of fatal pedestrian crash occurrence increased at night, in unlit locations, and during adverse weather conditions. In addition, the likelihood of a fatal crash decreases when medians exist on roads and around institutional land use. Also, the chance of fatal crashes increased on straight and flat roads and at locations with more bus stops. Finally, this study explored spatial variation in the effect intensity of these significant variables across the study area using the GWLR technique. High intensity variation across the study area was found for road geometry and institutional land use factors. On the other hand, low intensity variation was found for light conditions and the presence of median factors. This technique can be applied in any area, and the results would help provide insights into the spatial dimension of traffic safety.

COVID-19 pandemic has caused adverse impacts on different aspects of life around the globe, including travelers’ mode choice behavior. To make their travel safe, transportation planners and policymakers need to understand people’s perceptions of the risk of COVID-19 transmission in different travel modes. This study aimed to estimate mode-wise perceived risk of viral transmission and identify the factors that influenced the perceived risk in Bangladesh. The study used a five-point Likert scale to measure the perceived risk of COVID-19 transmission in each travel mode. Using ordinal logistic regression models, the study explored the factors that influenced the perceived risk of COVID-19 transmission in different travel modes. The study found that people perceived a very high risk of viral transmission in public transport (bus), moderate risk in shared modes (rickshaw, auto-rickshaw, ridesharing), and very low risk in private modes (private car, motorcycle/scooter, walking, cycling). Such high-risk perception of viral transmission in public transport and shared modes might lead to a modal shift to private modes, which would worsen urban transport problems and undermine sustainable transportation goals. The study also found that socio-economic factors (gender, age, income) significantly influenced perceived risks in all travel modes. Contrarily, psychological factors (worry, care, and trust) were significant only for public and shared modes, but not for private modes. Lastly, travel behavior-related factors influenced perceived risk in shared and private modes.

Researchers have been trying to identify the contributory factors behind pedestrian crash occurrences through studies at both microscopic and macroscopic levels. However, built environment-related factors have primarily been examined in developed countries, resulting in a limited understanding of the phenomenon in the context of developing countries. Methodologically, these studies mostly used global regression models, which failed to incorporate spatial autocorrelation and spatial heterogeneity. Additionally, some of these studies applied spatial regression models randomly without following a comprehensive logical framework behind their selections. Our study aimed to develop a comprehensive spatial regression modeling framework to examine the relationships between pedestrian crash occurrences and the built environment at the macroscopic level in a megacity, Dhaka, the capital of a developing country: Bangladesh. Using secondary pedestrian crash data, the study applied one global non-spatial model, two global spatial regression models, and two local spatial regression models following a comprehensive spatial regression modeling framework. The factors which significantly contributed to pedestrian crash occurrences in Dhaka were employed person density, mixed and recreational land use density, primary road density, major intersection density, and share of non-motorized modes. Except for the last factor, all the other ones were positively related to pedestrian crash density. Among the five models used in this study, the multiscale geographically weighted regression (MGWR) performed the best as it calibrated each local relationship with a distant spatial scale parameter. The findings and recommendations presented in this study would be useful for reducing pedestrian crashes and choosing the appropriate modeling technique for crash analysis.

The COVID-19 pandemic has caused incredible impacts on people’s travel behavior. Recent studies suggest that while the demand for public transport has decreased due to passengers’ inability to maintain physical distance inside this mode, the demand for private automobile and active transport modes (walking and cycling) has increased during the pandemic. Policymakers should take this opportunity given by the pandemic and encourage people to use active transport more in the new normal situation to achieve sustainable transportation outcomes. This study explores the expected change in active transport mode usage in the new normal situation in Bangladesh based on the data from a questionnaire survey. The study finds that 56% and 45% of the respondents were expected to increase travel by walking and cycling, respectively, during the new normal situation. On the other hand, 19% of the respondents were expected to do the opposite. The study further identifies the factors influencing the expected change in travel by active transport modes during the new normal situation by developing multinomial logistic regression models. Finally, this study proposes policies to increase active transport use beyond the pandemic and ensure sustainable mobility for city dwellers and their well-being.

The impacts of COVID-19 on the transportation system have received attention from researchers all over the world. Initial findings reveal that patronage of public transport has gone down, while the use of active transport has increased in general. To the best of our knowledge, no study has focused on the pandemic’s effects on motorcycle mode, let alone in the context of an Asian city. We attempted to fill this void in literature by investigating if COVID-19 has influenced people to purchase motorcycles and determining the factors driving their intentions. The study is based on an online survey of 368 people in Dhaka, the capital of Bangladesh. The study found that around 46% of the respondents were expected to increase travel by motorcycle during the post-lockdown period. About 21% of the respondents were also expected to do the opposite. Around 31% of the respondents planned to purchase a motorcycle by August 2021, and the results indicated that the pandemic has influenced more people to purchase motorcycles compared to the pre-pandemic period. The study further identified factors that influenced the respondents’ plan for purchasing a motorcycle during the post-lockdown period applying the binary logistic regression. Based on the findings of the study, policy measures were proposed for controlling the growth of motorcycle numbers and increasing the use of active transport modes as its alternative, and consequently, helping to achieve sustainable transportation outcomes.

Background: COVID-19 pandemic outbreak is an unprecedented shock throughout the world, which has generated a massive social, human, and economic crisis. Identification of risk factors is crucial to prevent the COVID-19 spread by taking appropriate countermeasures effectively. Therefore, this study aimed to identify the potential risk factors contributing to the COVID-19 incidence rates at the district-level in Bangladesh. Method: Spatial regression methods were applied in this study to fulfill the aim. Data related to 28 demographic, economic, built environment, health, and facilities related factors were collected from secondary sources and analyzed to explain the spatial variability of this disease incidence. Three global (ordinary least squares (OLS), spatial lag model (SLM), and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) regression models were developed in this study. Results: The results of the models identified four factors: percentage of the urban population, monthly consumption, number of health workers, and distance from the capital city, as significant risk factors affecting the COVID-19 incidence rates in Bangladesh. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh, with an R2 value of 78.6%. Conclusion: Findings and discussions from this research offer a better insight into the COVID-19 situation, which helped discuss policy implications to negotiate the future epidemic crisis. The primary policy response would be to decentralize the urban population and economic activities from and around the capital city, Dhaka, to create self-sufficient regions throughout the country, especially in the north-western region.

This study investigates existing users’ attitudes towards public transport from two perspectives. First, the effects of accessibility to various destinations and ease of access to terminals on public transport users’ attitudes are determined. Second, the contribution of social norm, as an information source, in the formation of users’ attitudes is assessed. A user-preference survey was undertaken in Auckland, New Zealand, at two terminals. Data were analyzed using ordinal and logistic regression models. Findings showed that residential density and quality of the built environment, particularly safety, have an effect on the number of pedestrians who access a terminal. Accessibility to various destinations, “reaching work/education,” and “reaching other suburbs” in both data sets were statistically significant for existing users’ satisfaction with the current system. The findings also show that negative experiences of others have an adverse effect on existing users’ intentions to continue ridership. Overall, the results showed that to retain existing patronage, the ease of access to terminals and connectivity to various destinations need to be of a high standard.

Travel Demand Management (TDM) techniques mainly focus on reducing the number of single occupant car trips and encourage people to use more efficient and sustainable modes such as buses, cycles, and walking. Travel plan is being used as a Travel Demand Management tool around the world in recent times. Medium to large organisations have started to develop travel plans for their employees with a view to modifying their travel behaviour. If the commuting behaviour of a large number of employees could be changed through travel plans it could have a huge impact on reducing congestion and road transport related pollution. Universities can attract a large number of trips from the students and staff. On many issues universities act as a role model. If the universities can modify the travel habits of students and staff through travel plans, they can not only have positive effects on the city road network and environment but also encourage other organisations to follow suit. This study critically reviews the travel plans of a few universities around the world from sustainability perspectives. It critiques the basis of adopting policies and attempts to find the common as well as unique features of the measures with a view to identifying the best practices. Travel plans are still a new concept and there are hardly any guidelines on preparing them. This research would help universities to modify or develop effective travel plans and lead others to adopt sustainable practices.