Automatic Accident Detection, Segmentation and Duration Prediction Using Machine Learning

Traffic accidents are often inaccurately reported, with incorrect location and disruption duration due to various external factors. This can result in imprecise predictions and inaccurate decision-making in data-driven models. To address these challenges, our study presents a comprehensive framework for traffic disruption segmentation from traffic speed data (obtained from Caltrans Performance Measurements system) in the time-space proximity of reported accidents (from Countrywide Traffic Accident dataset). Furthermore, we evaluate multiple machine learning models on reported, estimated, and manually marked disruption intervals, and demonstrate that our enhanced modelling approach reduces the root mean squared error (RMSE) of traffic accident duration prediction while providing higher similarity with disruptions observed in traffic speed. Our algorithm yields higher disruption detection precision than reported accident timelines. Although using multiple segments offers a slight decrease in the quality of results, it highlights more disruptions. Future research could explore expanding the algorithm's complexity and applying it to improve traffic incident impact predictions.