Research on the Law and Prediction of Traffic Accidents Based on Value at Risk in the Context of Intelligent Transportation - Cheng, Zihang; Shao, Zihan; Wang, Xinyi (Transactions on Computer Science and Intelligent Systems Research, 2024)
The research introduces traffic accident predictive models known as the Convolutional Long Short-Term Memory Model and a K-means cluster algorithm with Random Forest Network, which aims to provide precise forecasts of incident rates Through the amalgamation of the strengths inherent in Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) neural network, K-means cluster algorithm and Random Forest algorithms. These fusion models surpass the performance of the standalone LSTM model, particularly excelling in the prediction of incident rates during driving hours and the location. The study's outcomes reveal that the proposed model exhibits remarkable proficiency in specific transformation domains, underscoring its superior efficacy compared to single conventional models. These results underscore the advantages of integrating multiple algorithms within a unified framework to enhance predictive accuracy significantly. Consequently, the Convolutional Long Short-Term Memory and K-means cluster algorithm with a Random Forest Network emerge as promising solutions for advancing incident rate forecasting capabilities.