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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.

Flooding has become a persistent and intensifying threat in Bangladesh, causing widespread damage to infrastructure and affecting large portions of the population each year. The increasing frequency and intensity of these events underscore the need for advanced methods to assess and predict flood severity effectively. This study aims to develop a robust machine learning model for accurately classifying flood severity in both multi-class and binary formats, specifically addressing imbalanced data challenges by utilizing historical weather and water level data. A systematic approach was employed, beginning with extensive data preprocessing to ensure quality and consistency. The dataset was then prepared in multiple formats (multi-class and binary) to capture different aspects of flood severity classification. To tackle class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to each format, enhancing model reliability. Multiple classification models were evaluated, including individual classifiers and ensemble techniques, with the stacking ensemble emerging as the top performer. This model achieved accuracies of 98.62% for multi-class and 98.87% for binary classification before SMOTE, improving to 99.89% and 99.14%, respectively, after applying SMOTE. These findings demonstrate the model's potential as an effective tool for flood severity prediction, with significant implications for enhanced risk management and disaster response strategies. Future research will focus on deploying this model for real-time flood alerts, aiming to bolster resilience and preparedness in flood-prone communities.

Climate-induced rural livelihood loss and consequent rural-urban migration is a common scenario in today's developing countries. However, little is currently known about the dynamics of the process of climate-displaced migration and the experiences of associated migrants. This is an attempt to understand how poor 'climate-induced' migrants perceive their urban conditions in hydro-geophysical and socio-economic terms. Dhaka City, the densely populated capital of Bangladesh, is highly vulnerable to the impacts of climate change. In future a sustained influx of climate-induced migrants is likely to join the ranks of the urban poor, where they will have to face new hazards of city life. Therefore this research has tried to answer questions such as to what extent have the climate-induced migrants' aspirations been fulfilled after migration and whether their vulnerability to different hazards is different than that of the non-climate-induced migrants. The research has termed them 'climate-induced migrants' who have migrated mainly due to problems of the type climate change is expected to cause; for example flood, cyclone, riverbank erosion, waterlogging, drought and salinity intrusion. Tracer survey and snowballing process were used to identify poor climate-induced migrants in Korail, one of the largest slums in Asia. Questionnaire surveys, focus groups and personal interviews were the main research methods. It argues that climatic factors never affected any other group so severely at both their origin and destination as it affected the poor climate-induced migrants. They face some hazards more severely than other types of migrants due to the differences in their financial and coping capacities and educational qualification. Finally the recently arrived illiterate female climate-induced migrants from a cyclone prone area have been identified as the most vulnerable population. With the rapid rate of urbanization and climate change, this is high time to identify such migrants and bring them under separate plans.