Multi-Modal Traffic Analysis: Integrating Time-Series Forecasting, Accident Prediction, and Image Classification

--This paper presents a comprehensive framework that integrates multiple machine learning techniques for advanced traffic analysis. Our approach combines (1) an ARIMA(2,0,1) model for time-series forecasting, achieving a Mean Absolute Error (MAE) of 2.1; (2) an XGBoost classifier for accident severity prediction with 100% accuracy on balanced datasets; and (3) a Convolutional Neural Network (CNN) architecture for traffic image classification, achieving 92% accuracy. These methods were rigorously tested on heterogeneous datasets, demonstrating significant improvements over baseline models. Feature importance analysis revealed key contributing factors, such as weather conditions and road infrastructure, to accident severity. This research lays the groundwork for future advancements in intelligent transportation systems. Urban traffic management is a critical challenge in modern cities, where growing populations and increasing vehicle densities exacerbate congestion and safety issues.