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INTRODUCTION: Globally, pedestrians are one of the most vulnerable road-user groups. Their vulnerability increases while crossing the road at controlled intersections during the "don't walk" phase. Previous literature shows that driver yielding behavior has an association with pedestrian safety at intersections. Though several studies have explored driver yielding behavior towards pedestrians at conflict points, evidence on how pedestrian actions influence driver yielding behavior at intersections is yet to be investigated. METHOD: To pursue this end, a binary logistic regression model was developed using the collected data to explore the effect of non-compliant pedestrian characteristics and their road crossing behavior on driver yielding behavior towards pedestrians at six controlled intersections of Dhaka, Bangladesh. The data were collected through videography survey. RESULTS: Results showed that drivers were more likely to yield to pedestrians who were female, crossing in a group, carrying baggage, not using a mobile, making some hand gesture to the driver, or crossing by rolling gap strategy. PRACTICAL APPLICATIONS: These findings add new insights for transportation planners into the complex interaction between vehicles and pedestrians at busy controlled intersections, and thus would help to make a pedestrian friendly street.

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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.

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BACKGROUND: To prevent the viral transmission from higher infected to lower infected area, controlling the vehicular traffic, consequently public movement on roads is crucial. Containment strategies and local cognition regarding pandemic might be helpful to control vehicular movement. This study aimed to ascertain the effectiveness of containment strategies and local cognition for controlling traffic volume during COVID-19 pandemic in Dhaka, Bangladesh. METHOD: Six containment strategies were considered to explore their influence on traffic condition, including declaration of general holiday, closure of educational institution, deployment of force, restriction on religious gathering, closure of commercial activities, and closure of garments factories. Newspaper coverage and public concern about COVID-19 were considered as local cognition in this research. The month of Ramadan as a potential event was also taken into account considering it might have an impact on the overall situation. Average daily journey speed (ADJS) was calculated from real-time traffic data of Google Map to understand the vehicular traffic scenario of Dhaka. A multiple linear regression method was developed to comprehend the findings. RESULTS: The results showed that among the containment strategies, declaration of general holiday and closure of educational institutions could increase the ADJS significantly, thereby referring to less traffic movement. Besides, local cognition could not significantly affect the traffic condition, although the month of Ramadan could increase the ADJS significantly. CONCLUSION: It is expected that these findings would provide new insights into decision-making and help to take appropriate strategies to tackle the future pandemic situation.

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BACKGROUND: The emergence of COVID-19 pandemic has not only shaken the global health sector, but also almost every other sector, including economic and education sectors. Newspapers are performing a significant role by featuring the news of COVID-19 from its very onset. The temporal fluctuation of COVID-19 related key themes presented in newspaper articles and the findings obtained from them could offer an effective lesson in dealing with future epidemics and pandemics. AIM AND METHOD: This paper intends to develop a pandemic management framework through an automated content analysis of local newspaper coverage of COVID-19 pandemic in Bangladesh. To fulfill the aim, 7,209 newspaper articles are assembled and analyzed from three popular local newspapers named "bdnews24.com", "New Age", and "Prothom Alo English" over the period from January 1, 2020 to October 31, 2020. RESULTS: Twelve key topics are identified: origin and outbreak of COVID-19, response of healthcare system, impact on economy, impact on lifestyle, government assistance to the crisis, regular updates, expert opinions, pharmaceutical measures, non-pharmaceutical measures, updates on vaccines, testing facilities, and local unusual activities within the system. Based on the identified topics, their timeline of discussion, and information flow in each topic, a four-stage pandemic management framework is developed for epidemic and pandemic management in future. The stages are preparedness, response, recovery, and mitigation. CONCLUSION: This research would provide insights into stage-wise response to any biological hazard and contribute ideas to endure future outbreaks.

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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 R 2 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.

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Though equal and easy accessibility to healthcare facilities are a fundamental right, people of the coastal region often get poor accessibility to healthcare facilities. This research aimed to assess the spatial accessibility to healthcare facilities in the coastal region of Bangladesh. Patuakhali district was selected as the study area. Accessibility to three levels of healthcare facilities: Community Clinic (CC), Upazila Health Complex (UHC) and District Hospital (DH) was measured individually using Geographic Information System (GIS) adopting the simple distance measures. Finally, overall accessibility to healthcare facilities was measured by overlaying accessibility to all three levels of healthcare facilities according to their relative importance. The findings of this study showed that a significant portion (70%) of Patuakhali district had high accessibility to CC; whereas, almost 60% and 40% of the area had poor accessibility to UHC and DH, respectively. Furthermore, 40% and 28% of the area of Patuakhali district had low and high accessibility to overall healthcare facilities, respectively. Furthermore, accessibility to healthcare facilities was found very poor in rural areas, char areas, and seashore. Thereafter, it is recommended to provide an UHC in every upazila and a DH in the southern part of Galachipa upazila to ensure high spatial accessibility of healthcare facilities.

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Dhaka, the capital and megacity of the developing country Bangladesh, has experienced a sharp rise in motorcycle users in the last decade, especially after the introduction of ridesharing services. Therefore, the morbidity and mortality rates of motorcycle crash injuries have also increased and become one of the major safety concerns. However, there is scant empirical evidence on motorcycle crash severity in the context of developing countries. Hence, this study was conducted to identify the factors that influenced the severity of motorcycle crashes in Dhaka. A binary logistic regression model was developed using motorcycle crash data of Dhaka over the period of 2006-2015 to identify the contributing factors of motorcycle crash severity. The model output showed that eleven factors significantly increased the probability of fatal motorcycle crashes. These factors were crashes occurring on weekends, during the rainy season, during dawn and night period, at non-intersections, on straight and flat roads, on highways, hit pedestrian type crashes, crashes involving motorcycles with no defect, crashes with heavier vehicles, crashes involving motorcyclists not wearing helmets, and drivers with alcohol suspicion. These findings would help to formulate prevention strategies to reduce the injury severity of motorcycle crashes in the developing countries.

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Although the rate of road crashes and their severity is relatively higher in developing countries, there is still a lack of research on pedestrian-vehicle crash severity in these contexts, particularly in Bangladesh. Therefore, this study aimed to identify the contributing environmental, road, and vehicular factors that influenced pedestrian-single-vehicle crash severity in Dhaka, a megacity and the capital of Bangladesh. A binary logistic regression model was developed in this study by analyzing a data set of pedestrian-single-vehicle crashes involving casualties in Dhaka from 2010 to 2015. The model identified seven significant factors influencing pedestrian-vehicle crash severity. Significant factors increasing the likelihood of fatal crashes included crashes during adverse weather, dawn/dusk period, night period (where street light was absent), off-peak period, crashes where road divider was unavailable, road geometry was straight and flat, and crashes those were occurred by heavier vehicles. Besides, crashes at three-legged intersections were less likely to be fatal. Both similarities and differences were found among the significant factors influencing pedestrian-vehicle crash severity in Dhaka from the findings of the developed countries. The findings of this study would help transport engineers and planners to design safer roadways for both pedestrians and vehicles.

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Pedestrian road-crossing strategy is one of the most important pedestrian road-crossing behaviors. The safety of the pedestrians often depends on it. Among the road-crossing strategies, rolling gap crossing strategy is the riskiest one. The objective of this research was to explore the factors that influenced pedestrians' decision to cross the road by rolling gap crossing at intersection. Data regarding road-crossing strategy of the pedestrians, their characteristics, their road-crossing behavior, intersection geometry, and traffic environmental condition were collected through videography survey method, on-site observation, and secondary source from six intersections of Dhaka, Bangladesh. A binary logistic regression model was developed in this study by using the collected data. Results of the developed model showed that seven statistically significant factors strongly influenced pedestrians' decision to cross the road by rolling gap crossing at intersections. These factors were intersection control type, median width, vehicle flow, available gap on the road, age group of the pedestrians, their crossing group size, and their behavior of crosswalk usage. The results of this study would help the policymakers to take proper interventions to alleviate pedestrian safety problems.

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