A Novel Approach of Traffic Congestion and Anomaly Detection with Prediction Using Deep Learning

Abstract

Traffic congestion and anomalies present significant challenges to urban mobility, impacting economic activity and quality of life. Traditional traffic management systems often fall short in accurately predicting and mitigating these issues due to their reliance on static models and limited data sources. This study introduces a novel deep learning framework designed to enhance traffic congestion and anomaly detection and provide accurate traffic flow predictions. Leveraging a comprehensive dataset encompassing various traffic patterns and conditions, we employ a convolutional neural network (CNN) model, renowned for its efficacy in handling spatial data, combined with long short-term memory (LSTM) networks to capture temporal dependencies. Our approach distinguishes itself by incorporating real-time data and employing advanced feature extraction techniques, enabling the dynamic adjustment of traffic management strategies. The methodology section outlines the data preprocessing steps, model architecture, training process, and evaluation metrics employed. Experimental results demonstrate the model's superior performance over existing methods in terms of accuracy, precision, recall, and computational efficiency. The discussion elaborates on the model's practical implications for smart city initiatives and its potential to revolutionize traffic management systems. This study not only addresses the gaps identified in the literature review but also opens avenues for future research in applying deep learning to urban traffic challenges.