Early and Reliable Traffic Accident Prediction via Two-Stream Cross Attention Fusion Network

Spatial Prediction And Safe Route Optimization of Autonomous Vehicle Accidents: Machine Learning And Gis-Based Integrated Approach

Autonomous vehicle technologies are reshaping traffic safety and urban mobility; however, software and sensor failures, legal uncertainties, and mixed traffic environments continue to pose significant accident risks. Recent fatal incidents during real-world testing underline the critical need for effective risk predict...

A Deep Spatio-Temporal Framework for Multi-Class Traffic Prediction and Accident Detection in Surveillance Video

Experimental results demonstrate that Z-score standardization improves classification performance, and the feasibility and robustness of the proposed framework in real-world traffic environments are indicated.

Road Accident Detection and Alert System using Deep Learning and Computer Vision

Explainable Artificial Intelligence and Computer Vision for RealTime Detection and Prediction of Critical Events in Smart Cities

This study presents XAI-CityVision, an explainable artificial intelligence platform that combines computer vision, object identification, temporal learning, risk assessment, and visual explanation that offers a repeatable architecture for integrating deployment-aware evaluation, operator-oriented explanations, and pred...

Multi-Model Traffic Congestion Prediction Using Diffusion Convolutional Recurrent Neural Networks with Transformer Based Fusion and Dual-Channel CNN for Multimodal Learning

Traffic congestion prediction focuses on evaluations upcoming road traffic states by analyzing past and real-time data such as vehicle velocity, traffic throughput and road occupancy. However, many existing models have limited adaptability to rapidly changing urban traffic conditions. To address these limitations, the...

An efficient computer vision framework for RSU-based accident recognition and V2X communication

Results show that the proposed framework can provide accurate and timely accident detection while supporting rapid V2X warning dissemination for next-generation intelligent transportation systems.