BDRoadRisk: Large-Scale GPS Traffic Accident Severity Dataset

Dataset Overview: This dataset contains 10 million synthetic GPS-based traffic event records simulating road conditions on the urban road networks of Dhaka and Chittagong, Bangladesh. Each record includes 23 features and is labelled with one of three accident severity classes: No_Accident, Minor, and Major. The features are designed to include both pre-accident and post-accident information, where pre-accident features describe environmental, road, and vehicle conditions before an accident, while post-accident features capture factors associated with the accident event and its severity. Severity labels are generated using a context-driven probabilistic model. A 1.8% boundary label noise is applied at the No_Accident/Minor boundary to reflect real-world reporting ambiguity. The dataset is provided in two formats: a Hive-partitioned Apache Parquet archive compatible with Apache Spark and distributed query engines, and a single flat CSV file suitable for Pandas and R. It is intended for multi-class severity classification benchmarking, explainability research, and big data pipeline development in intelligent transportation systems. The attached data dictionary provides detailed descriptions of all features, including their categorization into pre-incident and post-incident groups. Dataset Composition: - Total Records: 10 million - Total Features: 23 - Target Class Labels: No_accident, Minor and Major. - Class Distribution: No_accident 74.41%, Minor 17.30%, and Major 8.28%. Features (Attributes): - latitude (Continuous decimal) - longitude (Continuous decimal) - speed_kmh (Continuous, road-class dependent) - acceleration_ms2 (Continuous) - visibility_km (Continuous) - temperature_celsius (Continuous, non-predictive) - traffic_density (Integer, non-monotonic) - vehicle_age_years (Integer, right-skewed) - driver_age (Integer, weak signal) - reaction_time_sec (Continuous, time-conditional) - weather_condition (Clear, Rain, Fog, Storm) - road_surface (Dry, Wet, Icy, Damaged) - junction_type (None, T-Junction, Crossroad, Roundabout) - light_condition (Daylight, Dusk_Dawn, Dark_Lit, Dark_Unlit) - time_of_day (Morning, Afternoon, Evening, Night) - vehicle_type (Car, Truck, Motorcycle, Bus, Van) - driver_experience_level (Novice < Intermediate < Experienced) - road_quality (Poor < Fair < Good) - speed_limit_zone (30 < 50 < 80 < 100 < 120 km/h) - alcohol_influence (None < Suspected < Confirmed) - seatbelt_worn (Yes > No) - month (1–12) - road_class (motorway, primary, secondary, residential) - accident_severity (No_Accident, Minor, Major) Research Implications: The dataset supports the development and validation of ML and DL models for post-incident traffic accident severity classification. Additionally, the dataset can be utilized for accident incident classification (No Accident vs. Accident) as well as accident severity classification (Minor vs. Major).