Research Article
Trend and Methods of IoT Sequential Data Outlier Detection
@INPROCEEDINGS{10.1007/978-3-031-50580-5_34, author={Yinuo Wang and Tao Shen and Siying Qu and Youling Wang and Xingsheng Guo}, title={Trend and Methods of IoT Sequential Data Outlier Detection}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV}, proceedings_a={ICMTEL PART 4}, year={2024}, month={2}, keywords={Time series Machine learning Internet of Things Outlier detection}, doi={10.1007/978-3-031-50580-5_34} }- Yinuo Wang
Tao Shen
Siying Qu
Youling Wang
Xingsheng Guo
Year: 2024
Trend and Methods of IoT Sequential Data Outlier Detection
ICMTEL PART 4
Springer
DOI: 10.1007/978-3-031-50580-5_34
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.
