整合预测、分类与视觉分析，提升交通系统智能水平
Multi-Modal Traffic Analysis: Integrating Time-Series Forecasting, Accident Prediction, and Image Classification
- 融合时间序列、分类与图像识别的多模态框架
- 事故严重性分类达100%准确率，图像识别准确率92%
- 适合智慧城市建设与实时交通监控场景
本研究提出一个集成机器学习框架，用于高级交通分析，结合时间序列预测、分类和计算机视觉技术。系统采用ARIMA(2,0,1)模型进行交通预测（MAE: 2.1），XGBoost分类器进行事故严重性分类（平衡数据下准确率100%），以及卷积神经网络（CNN）进行交通图像分类（准确率92%）。在多种数据集上测试，该框架优于基线模型，并识别出影响事故严重性的关键因素，包括天气和道路基础设施。其模块化设计支持部署于智慧城市系统中，实现实时监测、事故预防与资源优化，助力智能交通系统发展。
原文摘要 · Abstract (English)
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.
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