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.

摘要

交通事故预测代表了世界各地交通管理机构的主要优先事项，以确保减少生命、财产和经济损失。越来越多的交通事故数据已被用于训练机器学习预测器，尽管由于事故相对罕见、交通事故在时间和空间上的相互依赖性以及对人类行为的高度依赖，这是一项具有挑战性的任务。最近，深度学习技术显示出比传统模型显着的预测改进，但在其适用性、准确性和提供实用信息的能力方面仍然存在一些困难和悬而未决的问题。