Multi-Modal Traffic Analysis: Integrating Time-Series Forecasting, Accident Prediction, and Image Classification
It addresses traffic monitoring and accident prevention for smart city systems, but is incremental as it combines existing methods.
This study tackled traffic analysis by integrating time-series forecasting, accident prediction, and image classification, achieving results such as a 2.1 MAE for traffic prediction and 100% accuracy for accident severity classification on balanced data.
This study proposes an integrated machine learning framework for advanced traffic analysis, combining time-series forecasting, classification, and computer vision techniques. The system utilizes an ARIMA(2,0,1) model for traffic prediction (MAE: 2.1), an XGBoost classifier for accident severity classification (100% accuracy on balanced data), and a Convolutional Neural Network (CNN) for traffic image classification (92% accuracy). Tested on diverse datasets, the framework outperforms baseline models and identifies key factors influencing accident severity, including weather and road infrastructure. Its modular design supports deployment in smart city systems for real-time monitoring, accident prevention, and resource optimization, contributing to the evolution of intelligent transportation systems.