Bangladesh Road Traffic Accident Dataset (2007–2024): Multi-Source Integration from ARI, BRTA, DMP, MP Unit.

Additionally, the primary dataset collected via forms captures incident details directly from field reports, ensuring high granularity and accuracy in recorded attributes. The dataset captures a rich variety of attributes, including: Temporal Information: Exact date, time, and year of each recorded accident. Geospatial Data: Detailed location descriptions with latitude and longitude coordinates for spatial analysis.

Incident Narratives: Text-based summaries describing the circumstances of each accident. Vehicle and Victim Data: Vehicle types, collision categories, number of vehicles involved, casualty counts, injury details, and victim classifications. Environmental Conditions: Light levels, weather conditions, and road surface status at the time of the incident.

Road and Infrastructure Attributes: Road classification, nature, connection type, surface condition, junction types, and traffic control measures. Accident Severity Indicators: Death and injury counts, as well as accident intensity classifications. Preliminary statistical and correlation analyses conducted on the dataset reveal generally weak linear relationships between predictive features and accident severity, suggesting that traditional linear modeling techniques may have limited predictive capability.

Consequently, advanced ensemble-based machine learning approaches, which can capture non-linear interactions, mitigate overfitting, and improve predictive robustness, are recommended for modeling and forecasting purposes. This dataset offers a robust empirical foundation for diverse research and policy applications, including traffic safety assessments, accident hotspot mapping, predictive risk modeling, urban transport infrastructure planning, and evidence-based road safety policy formulation.

The inclusion of both institutional records and primary field-collected data ensures a level of depth, reliability, and coverage that makes this dataset an invaluable resource for academics, policymakers, urban planners, and data scientists addressing road safety challenges in Bangladesh.