Traffic accidents significantly impact societies and economies. The risk of collision is highest in urban areas, leading to devastating loss of life and escalating socioeconomic costs. In this context, numerous studies have focused on accurately predicting accident risk, severity, and duration using various methodologies. This paper presents an overview of traditional statistical models for accident prediction and a comprehensive systematic review of the literature on statistical modeling, machine learning (ML), and deep learning (DL) techniques employed in this field. Different methodologies and techniques are compared by categorizing studies that adopt similar approaches and analyzing them comparatively. Furthermore, a distinction is made between temporal and spatiotemporal models to describe how these approaches influence the accuracy of future predictions regarding accident occurrence and the duration of impact. This review distinguishes itself from similar works by not only comparing models and approaches, but also by analyzing how external features, such as meteorological data, road geometric design, and land usage, affect the probability of accidents and the models’ accuracy in forecasting road safety. The study explores the performance levels and limitations associated with a set of forecasting approaches, offering an analytical discussion of their differences and similarities, and potential future developments in this research space, including the use of hybrid models and reinforcement learning (RL). The results of this review indicate that DL models tend to be better suited to complex forecasting problems due to their superior ability to represent features and extract non-linear spatiotemporal correlations. This article concludes by describing various directions for further research, ranging from optimizing model architectures to integrating real-time big data into proactive prediction systems.
Affou et al. (Wed,) studied this question.