The Dhaka Metro Rail (DMR) has been constructed as part of the Dhaka Transport Coordination Authority’s 20-year Strategic Transport Plan to reduce traffic congestion in Dhaka, the capital city of Bangladesh. The DMR is the first urban rail transit system in Bangladesh and has the potential to change the existing modal share. Commuters have mixed responses about the daily commuting on the DMR and mode choice behavior. This study analyses the commuters’ perception of a modal shift towards the DMR by applying the theory of planned behavior (TPB). The structural equation model (SEM) used environmental concern and socio-demographic factors as additional explanatory variables to improve the explanatory capability of the TPB. A questionnaire survey was administrated using an online survey and validated via an in-person interview to collect the commuters’ responses. The results reveal that environmental concern is the most significant contributor to commuters’ perceptions of a modal shift towards the DMR in Dhaka City, followed by the attitude and perceived behavioral control of the respondents. The findings offer valuable insights for the DMR and other mass rapid transit service operators for policy-making in Dhaka and other cities with similar socio-economic demographics in the world.

Dhaka Mass Rapid Transit (MRT) system is the first-ever rapid transit system in Bangladesh, implemented with the aim of mitigating the acute traffic congestion in Dhaka City by providing a dependable transportation alternative to personal automobiles. In order to assess the performance and service quality of this newly launched metro system, a survey utilizing a questionnaire format was employed, wherein data was collected via both online and in-person interactions. This questionnaire employed a total of fifteen independent variables along with one dependent and seven demographic attributes. The dataset was subjected to analysis through the utilization of Ordinal Logistic Regression (OLR). The study provides significant perspectives on the attributes that impact the service quality of the Dhaka Mass Rapid Transit (MRT) system. OLR elucidate that among the fifteen factors, nine factors are positively significant on Overall Service Quality (OSQ) of Dhaka MRT namely Train Availability, Crowdedness in Train, Crowdedness in Station, Ticket Fare, Cleanliness in Train, Cleanliness in Station, Washroom Facilities in Station, Courtesy of The Employees, Co-Passengers’ Behavior. The OLR model results also depict that Co-Passengers Behavior is the most important and Ticket Fare is the least important attribute among the significant variables which affect the OSQ. The findings of this research provide valuable insight for evidence-driven decision-making in the realm of urban transport planning and policy development. By prioritizing the improvement of service quality within the Dhaka Mass Rapid Transit (MRT) system, it is feasible to establish a more sustainable, effective, and user-oriented public transportation network in Dhaka City, thereby enhancing the general standard of living for its inhabitants.

Motorcycle crashes in Bangladesh pose a growing public health and transportation safety challenge, worsened by persistent gaps and availability in official crash data. This study uses a news media mining approach to examine 475 motorcycle-related crash reports from English-language Bangladeshi online newspapers published in 2022, forming a corpus of 50,190 words. Combining text mining with Structural Topic Modeling (STM) approach, the study identifies key crash patterns and latent themes within unstructured narratives. Preliminary analyses reveal frequent references to severe injuries, emergency response, young riders, fixed-object collisions, and nighttime crashes. Rapid Automatic Keyword Extraction and word co-occurrence network analyses underscore high-risk behaviors such as speeding and distraction, vulnerability of young male riders and students, and the influence of rural roads, highways, and urban environments. STM results indicate two dominant crash profiles: fatal single-vehicle crashes in rural settings linked to loss of control, and multiple-vehicle crashes in urban or highway areas often involving heavy vehicles. Guided by the Safe System Approach, the study proposes context-specific countermeasures, including infrastructure improvements, lane discipline enforcement, youth-focused education, improved vehicle visibility, and stronger emergency medical services. Overall, the findings demonstrate that online news media provide a scalable and informative source for motorcycle safety research in data-limited settings, supporting targeted strategies.

This study investigates pedestrian–vehicle crashes using explainable machine learning (ML) models and SHapley Additive exPlanations (SHAP) tool to understand the role of pedestrian actions in fatal/severe injury. The dataset consists of 4,343 at-fault pedestrian crashes (2017–2021) in Louisiana state, covering four types of pedestrian actions including crossing at intersection (1,592 crashes), crossing at midblock (1,612 crashes), walking with traffic (820 crashes), and walking against traffic (319 crashes). ML-based models, including gradient boosting machine (GBM), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), adaptive boosting (AdaBoost), and light gradient boosting (LightBoost), were employed to identify top factors. Based on predictive accuracy measures -accuracy, precision, recall, F1, G-Mean, Area Under Receiver Operating Characteristic (AUROC) curve. GBM performed better for “crossing at intersection” and “walking with traffic,” while CatBoost performed better for “crossing at midblock,” and “walking against traffic.” Key findings revealed that dark conditions (with or without streetlight), high-speed settings (40–55 mph), and pedestrian characteristics (>64 years, Caucasian race) were associated with an increased likelihood of fatal/severe injury while crossing at the intersection. For midblock crossing-related crashes, top factors were driver alcohol impairment, highway road, dark-no-streetlight, and posted speed limit of 50–55 mph. Factors such as open country, pedestrian/driver alcohol impairment, 50–55 mph speed limit, two-way road with physical separation, and older pedestrians were found to be associated with increased likelihood of fatal/severe injury while walking with traffic. For walking against traffic, top contributing factors were male drivers, young drivers, presence of passengers, weekend, business/industrial areas, dark-no-streetlight, and pedestrians aged 25–40 years.