A Comprehensive Review on Deep Learning Models for Traffic Accident Prediction Using Vehicle Trajectory Analysis

Abstract

The human and monetary costs associated with tra!c accidents make them one of the world’s most intractable problems. Worldwide, 1.35 million people lose their lives in automobile accidents each year (WHO, 2023), making them a leading cause of mortality. The medical bills and lost wages also cost billions of dollars due to these incidences. The fast urbanization and increase in the density of vehicles especially in developing economies such as India, further aggravate the necessity of predictive systems that have the potential to predict and avert accidents. This paper gives a thematic review of how recent techniques are used to predict tra!c accidents based on vehicle trajectory data. We include both classical statistical models and machine learning algorithms, as well as state-of-the-art deep learning architectures. We also compare the e”ectiveness of these methods to process complex spatio-temporal data, and we comment on the new methods, including Graph Convolutional Networks (GCNs), spatio-temporal Long Short-Term Memory networks (LSTMs), and Transformer-based models. Also, our survey reveals that there are still some issues to work on, pertaining to this area, such as a lack of generalization to various tra!c conditions, the lack of real-time integration, and privacy limitations. Lastly, we suggest future improvement of the creation of accident prediction frameworks of intelligent transportation systems that are interpretable, adaptable, and privacy conscious.