Multi-Modal Traffic Analysis: Integrating Time-Series Forecasting, Accident Prediction, and Image Classification
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913806614003712 |
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| author | M, Nivedita S, Yasmeen Shajitha |
| author_facet | M, Nivedita S, Yasmeen Shajitha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Modal Traffic Analysis: Integrating Time-Series Forecasting, Accident Prediction, and Image Classification M, Nivedita S, Yasmeen Shajitha Machine Learning 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. |
| title | Multi-Modal Traffic Analysis: Integrating Time-Series Forecasting, Accident Prediction, and Image Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.17232 |