Meta Analysis of Traffic Safety Prediction
Hongyu Yan, Jianbo Li, Zhihao Xu, Zhiqiang Lv · 2024
Predicting traffic safety trends is of great significance for the realization of autonomous driving and other related areas. Current research focuses on using machine learning and deep learning models to predict traffic safety issues, such as whether a vehicle will collide or the severity of a collision. However, traffic safety is influenced by multiple factors, and relying solely on primary traffic feature data for predictions is far from sufficient. Predictions about traffic safety should consider as many factors as possible. This paper summarizes five main factors: besides primary traffic features, others include weather and environmental features, road design and configuration features, socio-economic features, and traffic violation records. Additionally, an improved meta-analysis method is introduced to quantify and evaluate the use of multiple factors, thereby identifying which factors have a greater impact on traffic safety. Meta-analysis can accurately integrate the methods and results of various studies to conclude. Thus, this paper provides a comprehensive, in-depth, and detailed review through the improved meta-analysis.