Outlier Detection based Model for Event Pattern Recognition in Vehicular Networks

Today, detecting outliers plays a crucial role in modern transportation systems, improving traffic management and road safety. This paper introduces a new outlier detection-based model for recognizing event patterns in Vehicular Ad hoc NETworks (VANETs). Our solution utilizes advanced techniques, combining outlier detection and multiclassification capabilities to enhance the resilience and reliability of transportation systems in dynamic and complex traffic scenarios. The approach involves three stages: data preprocessing and feature extraction, outlier detection, and multiclassification. In the first stage, an image-based dataset is transformed into a feature-based dataset after essential preprocessing operations, such as feature extraction by analyzing local patterns in the image pixels using the Local Binary Patterns (LBP) method. In the second stage, a hybrid classification model based on a neural network is proposed to identify outlier events in real-time vehicle data, followed by the employment of machine learning models to classify them as normal or abnormal. When an abnormal traffic situation is detected, the final stage utilizes multiclassification deep neural networks, specifically ResNet and Inception, to categorize events into predefined classes. Through extensive simulations using real VANET data, we have demonstrated the relevance and accuracy of our model in recognizing event patterns in traffic systems compared to other existing techniques.