Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis.
By A. Parsa, A. Movahedi, Homa Taghipour, Sybil Derrible, A. Mohammadian. Accident Analysis and Prevention, 2019
944 citations recorded by the source corpus.
Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)-a Machine Learning (ML) technique-to detect the occurrence of accidents using a set of real time data comprised of traffic, network, demographic, land use, and weather features. The data used from the Chicago metropolitan expressways was collected between December 2016 and December 2017, and it includes 244 traffic accidents and 6073 non-accident cases. In addition, SHAP (SHapley Additive exPlanation) is employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can detect accidents robustly with an accuracy, detection rate, and a false alarm rate of 99 %, 79 %, and 0.16 %, respectively. Several traffic related features, especially difference of speed between 5 min before and 5 min after an accident, are found to have relatively more impact on the occurrence of accidents. Furthermore, a feature dependency analysis is conducted for three pairs of features. First, average daily traffic and speed after accidents/non-accidents time at the upstream location are interpreted jointly. Then, distance to Central Business District and residential density are analyzed. Finally, speed after accidents/non-accidents time at upstream location and speed after accidents/non-accidents time at downstream location are evaluated with respect to the model's output.
Subfields: Building and Construction, Civil and Structural Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/e3feb314a07f37e07b5b9807de0d88184ad0cebd
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In Civil and Structural Engineering
Citations in the corpus
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