Traffic Accident Hotspot Prediction Using Temporal Convolutional Networks: A Spatio-Temporal Approach

Predicting traffic accident hotspots is crucial for ensuring public safety, improving transport planning, and reducing transportation costs. Traditional deep learning models, such as Transformers and LSTMs, have been successful in this field but fail to integrate critical attributes essential for accurate prediction. To address these limitations, we propose utilizing a Temporal Convolutional Network (TCN), which efficiently learns spatial, temporal, and other external factors integral to accident hotspot prediction. Our proposed TCN architecture 1 demonstrate superior performance over state-of-the-art methods, offering valuable insights for proactive accident mitigation.