Abstract
In recent years, the state has made great efforts to develop the transportation industry. With the continuous expansion of the transportation network and the large-scale increase of vehicles, traffic congestion is serious, and traffic accidents occur frequently, which damages the normal traffic order. In order to ensure the overall operation of urban traffic is safer and more coordinated, it is of great practical value to detect abnormal traffic events in urban operation in real-time. Effective traffic incident detection may reduce traffic congestion brought on by traffic incidents, stop the incidence of follow-up accidents, and improve the safety of highway traffic. It has become a general trend to detect and warn about traffic accidents beforehand. This paper aims to build a machine-learning model to study the anomaly detection of traffic accidents. This study detected the number of traffic accidents in different time periods, and the traffic anomalies in 406 days every five minutes were analyzed. The frequent periods of accidents were statistically sorted out, which determined the basic direction for the prevention and detection of traffic accidents, helped to reduce traffic accidents, and improve people’s travel experience.
This work is supported by Shandong Key Technology R&D Program 2019JZZY021005 and Natural Science Foundation of Shandong ZR2020MF067.
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Wang, Y., Shen, T., Qu, S., Wang, Y., Guo, X. (2024). Trend and Methods of IoT Sequential Data Outlier Detection. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_34
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