Enhancing road safety through advanced predictive analytics in V2X communication networks

Abstract

"SafeRouteX," which stands for "Enhancing Road Safety through Advanced Predictive Analytics in V2X Communication Networks," is a novel approach to Vehicle-to-Everything (V2X) networks. Despite advancements in vehicle and road safety technologies, traffic accidents continue to be a significant problem. To increase road safety, SafeRouteX uses cutting-edge machine learning to forecast traffic accidents in V2X networks. The first data is processed by a data ingestion layer, and then the temporal patterns and uncertainties in the traffic data are extracted using LSTM-VAE features. The Elastic Net Layer minimizes dimensionality while retaining predictive power after feature selection optimization. A RandomForest classifier, which performs well on complicated, high-dimensional datasets, is the main predictive model. Combining approaches guarantees a solid strategy for the complexity analysis of traffic data. Using 4-fold cross-validation, the accuracy and generalizability of SafeRouteX were thoroughly evaluated across a range of traffic scenarios. This validation approach demonstrated SafeRouteX's ability to anticipate traffic accidents, highlighting its potential to enhance road safety in smart transportation systems.