Accident Concentration Behavior

Accident concentration behavior research aims to understand and predict the spatial clustering of traffic accidents to improve road safety. Current research heavily utilizes machine learning, employing algorithms like Random Forests, Multi-layer Perceptrons, and deep neural networks (including transfer learning approaches) to analyze diverse data sources, such as accident records, weather data, and street view imagery, to identify accident hotspots and predict their likelihood. This work is significant because it enables targeted interventions, improving the efficiency of safety measures and potentially reducing accident rates, particularly for vulnerable road users like pedestrians.