Real-time traffic accidents post-impact prediction: Based on crowdsourcing data

Accident; Analysis and Prevention
Yunduan Lin, Ruimin Li

Abstract

Traffic accident management is a critical issue for advanced intelligent traffic management. The increasingly abundant crowdsourcing data and floating car data provide new support for improving traffic accident management. This paper investigates the methods to predict the complicated behavior of traffic flow evolution after traffic accidents using crowdsourcing data. Based on the available data source, the traffic condition is divided into four levels by congestion delay index: severely congested, congested, slow moving and uncongested. Four types of accidents are consequently defined based on the occurrence of each level. A hierarchical scheme is designed for identifying the most congested level and sequentially predicting duration of each level. The proposed model is validated using traffic accident data in 2017 from an anonymous source in Beijing, China by embedding three machine learning algorithms, random forest (RF), support vector machine (SVM) and neural network (NN), in the scheme. The results show NN outperforms the other two models when the assessment is conducted in absolute differences. Meanwhile, RF has a slightly better performance than SVM, especially when predicting the short-period congestion of severely cong...Continue Reading

References

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Dec 9, 2014·Accident; Analysis and Prevention·Ruimin LiMoshe E Ben-Akiva
Jan 14, 2016·Accident; Analysis and Prevention·Qi ShiJaeyoung Lee

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Citations

Jan 19, 2021·Accident; Analysis and Prevention·Farman AliKyung-Sup Kwak

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