Predicting the duration of traffic incidents for Sydney greater metropolitan area using machine learning methods

• URL: http://arxiv.org/abs/2406.18861v2
• Date: Fri, 5 Jul 2024 03:03:45 GMT
• Title: Predicting the duration of traffic incidents for Sydney greater metropolitan area using machine learning methods
• Authors: Artur Grigorev, Sajjad Shafiei, Hanna Grzybowska, Adriana-Simona Mihaita,
• Abstract summary: This research presents a comprehensive approach to predicting the duration of traffic incidents and classifying them as short-term or long-term. We train and evaluate a variety of advanced machine learning models including Gradient Boosted Decision Trees (GBDT), Random Forest, LightGBM, and XGBoost. XGBoost and LightGBM outperform conventional models with XGBoost achieving the lowest RMSE of 33.7 for predicting incident duration and highest classification F1 score of 0.62 for a 30-minute duration threshold.
• Score: 3.3373764108905446
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This research presents a comprehensive approach to predicting the duration of traffic incidents and classifying them as short-term or long-term across the Sydney Metropolitan Area. Leveraging a dataset that encompasses detailed records of traffic incidents, road network characteristics, and socio-economic indicators, we train and evaluate a variety of advanced machine learning models including Gradient Boosted Decision Trees (GBDT), Random Forest, LightGBM, and XGBoost. The models are assessed using Root Mean Square Error (RMSE) for regression tasks and F1 score for classification tasks. Our experimental results demonstrate that XGBoost and LightGBM outperform conventional models with XGBoost achieving the lowest RMSE of 33.7 for predicting incident duration and highest classification F1 score of 0.62 for a 30-minute duration threshold. For classification, the 30-minute threshold balances performance with 70.84% short-term duration classification accuracy and 62.72% long-term duration classification accuracy. Feature importance analysis, employing both tree split counts and SHAP values, identifies the number of affected lanes, traffic volume, and types of primary and secondary vehicles as the most influential features. The proposed methodology not only achieves high predictive accuracy but also provides stakeholders with vital insights into factors contributing to incident durations. These insights enable more informed decision-making for traffic management and response strategies. The code is available by the link: https://github.com/Future-Mobility-Lab/SydneyIncidents

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