Real-Time Traffic Analysis Using Vehicle Trajectory Similarity in Edge Computing

The majority of road accidents occur as a result of driver irresponsibility. Utilizing deep learning models to analyze traffic CCTV can effectively mitigate traffic accidents. This paper aims to identify and monitor vehicles and assess traffic conditions by comparing and categorizing the similarities between vehicle trajectories. Three similarity measurement methods are employed: cosine similarity, Jensen-Shannon divergence, and Euclidean distance similarity. The results show that lanes and traffic patterns can be effectively identified using the presented approach. Through our approach, one can further develop traffic monitoring and analysis advancements, thereby enhancing road safety and traffic management