Traffic Accident Prediction based on CNN and Time of Commuting after Accident

Abstract

To address the challenges associated with traffic accident detection and the subsequent impact on commute times, a method combining CNN and Linear Regression models is proposed. Initially, this approach involves classifying specific accident data using images and detailed accident records. The images are then categorized into binary groups—accident and non-accident. Post-accident detection does not necessitate high-speed detection or extensive data processing; CNN are sufficiently capable for this purpose. Traffic accidents are an inevitable part of daily driving and can lead to significant delays in commuting. In rare cases, accidents may result in participants falling into a coma, further delaying rescue efforts. The proposed method aims to enhance road safety and traffic efficiency by promptly identifying accidents and minimizing the disruption caused to commute times through advanced technological solutions. By leveraging CNN for image analysis and Linear Regression for data modeling, this approach seeks to streamline accident detection processes, thereby improving overall traffic management and safety measures. This methodological framework underscores the importance of integrating cutting-edge technology to mitigate the adverse effects of traffic accidents on daily commuting.