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.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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