To ensure the safety of pedestrians, it is essential to have a comprehensive understanding of their road crossing behaviors, including the factors that influence the decisions they make regarding crossing. One of the crucial crossing behaviors of pedestrians is the crossing pattern, which refers to whether a pedestrian crosses the road by walking or running. Safety of the pedestrians often depends on it as running crossing pattern is one of the riskiest crossing behaviors. However, there is a lack of inclusive studies that explore pedestrians’ decision regarding their crossing pattern. Therefore, this study aimed to identify the significant factors influencing pedestrians' decision regarding their crossing patterns (walk or run) at intersections in Dhaka, Bangladesh, using the chi-square test, and to examine the association between the identified contributory factors and crossing pattern using the association rules mining technique. Pedestrian road crossing behaviors, their characteristics, and traffic characteristics related data were collected from six busy intersections in Dhaka using the videography survey method. Findings of the study showed that walking crossing pattern was strongly associated with the factors such as controlled intersection, narrow road, wide median, female pedestrian, older pedestrian, using two-stage strategy, group crossing, accepting larger gap, using crosswalk, and crossing in front of slower vehicles. Besides, running crossing pattern was strongly associated with uncontrolled intersection, wide road, narrow median, male pedestrian, younger pedestrian, using rolling gap strategy, crossing alone, accepting shorter gap, crossing through conflict zone, and crossing in front of light and faster vehicles. The findings of this study would aid policymakers to develop effective solutions to improve pedestrian safety as well as to design future technologies like automated driving systems.

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.

Pedestrian crashes at high-speed locations are a persistent road safety concern. Driving at high speed indicates that the driver would get considerably less time to react and make evasive maneuvers to avoid a pedestrian crash. On top of this, other crash-contributing factors such as humans (pedestrians or drivers), vehicles, roadways, and surrounding environmental factors actively interact together to cause a crash at high-speed locations. The pattern of pedestrian crashes also differs significantly according to the high-speed intersection and segment locations which require further investigation. This study applied Association Rules Mining (ARM), an unsupervised learning algorithm, to reveal the hidden association of pedestrian crash risk factors according to the high-speed intersection and segments separately. The study used Louisiana pedestrian fatal and injury crash data (2010 to 2019). Any crash location with a posted speed limit of 45 mph or above is classified as a high-speed location. Based on the generated association rules, the results show that pedestrian crashes at the high-speed intersection are associated with intersection geometry (3-leg) and control (1 stop, no traffic control device), driver characteristics (careless operation, failure to yield, inattentive-distracted, older, and younger driver), pedestrian-related factors (violations, alcohol/drug involvement), settings (open country, residential, business, industrial), dark lighting conditions and so on. Most pedestrian crashes at high-speed segments are associated with roadways with no physical separation, dark-no-streetlight conditions, open country locations, interstates, and so on. The findings of the study may help to select appropriate countermeasures to reduce pedestrian crashes in high-speed locations.

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.

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.

It is critical to understand the elements that influence CO2 emissions from commuting to establish low-carbon transportation and land-use regulations. Research attempted to determine the mechanisms by which the built environment (BE) influences commuting-related CO2 emissions. Most research was conducted in developed nations and used traditional modeling to evaluate the relationship between BE and CO2 emissions primarily considering direct consequences related with BE. There is a research vacuum in predicting the total impacts of BE on CO2 emissions from commuting, taking into account the mediating effect of car ownership. This research examines total effects of the built environment (BE) on commuting-related CO2 emissions including both direct and indirect effects. We used 10,592 home-based work trips from Dhaka, a developing city, to create a structural equation model (SEM) for predicting this association. We included car ownership as a mediating variable and treated BE, car ownership, and CO2 emissions as endogenous variables. Both population density and land-use diversity are positively associated with private car ownership. The study shows the built environment plays a different role in explaining CO2 emissions from commuting in developed and developing countries. Population density has a direct positive impact on CO2 emissions, as evidenced by previous research in a developing metropolis. Because of its mediating effect on car ownership, land use diversity has a considerable positive indirect impact on emissions but a negligible overall effect, making it ineffective on its own and necessitating the implementation of complementing Travel Demand Management (TDM) policies. Our modeling results are comparable with those from both developing and developed countries in terms of public transportation accessibility, job accessibility, and road network design. The results might be used to produce policy guidelines to reduce car ownership and CO2 emissions, which would help developing countries in South Asia meet their sustainable development goals.

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.

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.

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.

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.

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.

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.

The available approaches for measuring accessibility are rigid and complex in nature, and mostly impractical for decision-makers as they require a large number of data, logistics support, and technical knowledge. Therefore, this study seeks to propose a flexible and practical approach for quantifying and ranking the accessibility to market facilities in rural areas. A three-stage multi-criteria decision-making (MCDM) approach is proposed to fulfill the objective. The first stage involves the identification of factors that influence the accessibility to rural market facilities. The next stage involves the use of the Constant-Sum Paired-Comparison Method (CSPCM) to determine the priority of each identified factor. The third stage adopts the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to quantify and rank the accessibility to market facilities of rural areas. To illustrate a real-world application, accessibility to market facilities in ten sub-districts of Bangladesh have been quantified and ranked by using this proposed approach. Results of this application support the claim that MCDM approach is a practical, flexible, and reliable approach that would better assist the policy-makers to identify poor accessible rural areas.

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.

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.

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.

Pedestrians are the most vulnerable road users and their risky behaviours make the situation worse. This study aimed to assess the crash risk associated with various pedestrian groups and their road crossing behaviours at intersections in Dhaka, Bangladesh. A two-stage, multi-criteria decision-making approach—analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS)—was used for risk assessment. In the first stage, five risk assessment criteria: nearest vehicle speed, nearest vehicle type, vehicle flow, interrupted by vehicle, and minimum gap were identified from the literature, and these criteria were weighted by the AHP. In the second stage, risk associated with various pedestrian groups and their road-crossing behaviours were quantified and ranked by the TOPSIS. The results indicate that male and young pedestrians had higher risk among pedestrian groups. Furthermore, an oblique crossing type, a running crossing pattern, mobile phone usage while crossing, carrying medium-weight baggage, a rolling gap crossing type, high crossing speed, and cross by using conflict zone were found as the riskiest behaviours. Besides, using crosswalk while crossing, single-stage and two-stage crossing types, perpendicular crossing type, and group crossing were found to be the safest behaviours.

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.

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.

The compliance behavior of pedestrians at controlled intersections is an important determinant of the number of crashes involving pedestrians at those intersections. The objective of this study was to explore compliance behavior of the pedestrians at vehicular traffic signals and traffic-police-controlled intersections in Dhaka, Bangladesh. Two types of compliance behavior were examined: compliance with vehicular traffic signals and traffic police direction, and compliance with crosswalk. First, factors influencing each compliance behavior of pedestrians were identified from the existing literature and correlation test results. With those identified factors, two discrete choice models were developed: a multinomial logistic (MNL) model for explaining the compliance behavior with vehicular traffic signals and traffic police direction, and a binary (BLR) model for exploring the compliance behavior with crosswalk. The results of the MNL model showed that compliance behavior was significantly associated with intersection control type, gender, crossing group, baggage handling by pedestrian, and vehicle flow. Whereas, the BLR model showed that compliance with crosswalk was significantly influenced by age of the pedestrians, compliance with intersection control direction by pedestrians, and vehicle flow. These findings would help the policy-makers to take countermeasures to alleviate traffic safety related problems.

Pedestrian crossing speed and waiting time are critical parameters for designing traffic signals and ensuring pedestrian safety. This study aimed to carry out microscopic level research on pedestrian crossing speed and waiting time at intersections in Dhaka. To fulfill this aim, crossing-related data of 560 pedestrians were collected from three intersections in Dhaka using a videography survey method. Descriptive and statistical analyses were carried out, and then two multiple linear regression (MLR) models were developed for these two parameters by using the collected data. From the results, 1.15 m/s was found to be the design pedestrian crossing speed. Results also show that the crossing speed of pedestrians was associated with intersection control type, gender, age, crossing type, crossing group size, compliance behavior with control direction, and crossing location. In case of waiting time, findings show that pedestrians did not want to wait more than 20–30 s to cross the road. Furthermore, the waiting time of the pedestrians varied with intersection control type, gender, age, minimum gap, waiting location, and vehicle flow. Findings of this study will help to alleviate traffic safety problems by designing an effective intersection control system.

A growing body of research shows positive association between parks and physical activity, but very few studies have investigated the characteristics of users and how the activities vary according to different user groups. The purpose of this study was to investigate the socio-demographic profile of the users, their activities and motivation of visiting the park. Total 400 users of Ramna Park were surveyed in face to face interview in different time periods. The participants were asked to provide information about their socio-economic profile, frequency and purpose of visit, mobility and activity patterns in the park and level of satisfaction about different facilities. At a random selection of the sample, we found larger proportion of male visitors than female visitors with a majority in the age group 40-60 years. The park has a large catchment area which extends beyond the range of walking distance and the frequency of visiting the park was found closely associated with the proximity of the users. Besides, no significant association was found between the proximity and duration of staying in the park. An overwhelming majority of the visitors come to the park for health purpose mainly for walking, jogging and physical exercise. The findings suggest that the purpose of visiting the park significantly varies according to the gender and age group of the respondents. The visitors were also asked about their satisfaction level and problems they usually faced based on their individual perception. Most of them raised their concerns for poor toilet facility and waste management.

About 60% of the trips are made on foot in Dhaka city but the pedestrians are the most vulnerable group among the road users. It is identified that more than 48% of reported road accidents and 72% of reported fatalities were pedestrians in Dhaka Metropolitan City. Among them, a large percentage of pedestrian crashes occurred in the intersection area. This study attempted to analyse the existing scenario of three accident-prone intersections of Dhaka city- Sonargaon-Panthapath, Jatrabari, and Shapla Chattar and redesign the intersections to ensure pedestrian safety. For the study, geographic elements were analysed and traffic studies were conducted for both vehicles and pedestrians of the intersections. Each of the legs of the intersections was incorporated in the study to know about the actual existing traffic scenario and problems of the intersections. After that, opinions of the stakeholders were collected regarding the redesign of the intersection. It was found that the geographical condition of the intersections was very poor. Sidewalks and roadways were mainly occupied by illegal hawkers, illegal parking, ticket counters. Besides, the roundabouts and central islands were not well-designed to control the vehicular and pedestrian traffic. So, these three intersections were redesigned by considering their land use, traffic flow, surroundings and the available standards. Overall pedestrian safety is expected to improve at those intersections if the proposed design is implemented.