A New Spatio-Temporal Neural Network Approach for Traffic Accident Forecasting

Abstract

Traffic accidents forecasting represents a major priority for traffic governmental organisms around the world to ensure a decrease in life, property, and economic losses. The increasing amounts of traffic accident data have been used to train machine learning predictors, although this is a challenging task due to the relative rareness of accidents, inter-dependencies of traffic accidents both in time and space, and high dependency on human behavior. Recently, deep learning techniques have shown significant prediction improvements over traditional models, but some difficulties and open questions remain around their applicability, accuracy, and ability to provide practical information. This paper proposes a new spatio-temporal deep learning framework based on a latent model for simultaneously predicting the number of traffic accidents in each neighborhood in Madrid, Spain, over varying training and prediction time horizons.