Expressway anomaly event recognition method based on clustering by fast search and find of density peaks(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2018年05期
- Page:
- 205-212
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- Expressway anomaly event recognition method based on clustering by fast search and find of density peaks
- Author(s):
- SUN Zhaoyun1; LI Wei1
-
- Keywords:
- traffic information and control engineering; intelligent transportation; anomaly event analysis; clustering by fast search and find of density peak; outlier detection; expressway fee data; data mining
- PACS:
- -
- DOI:
- -
- Abstract:
- To sense the expressway traffic operationstatus more accurately and comprehensively, a data mining method for identifying abnormal traffic events on an expressway using mass data collection was proposed. First, fee data from January 2017 were selected from the massive data available for the Guizhou Expressway toll. The data on the specific entrance and exit stations were selected, and some redundant fields were deleted, with those data only related to this study being retained. The time for driving into the entrance station and driving out of the exit station was used to calculate the vehicle staying time between the two toll stations. The selected data were analyzed based on the driving time and axle weight using a fast peak clustering algorithm. The distance between each data point was calculated, and the distance matrix was used as the input of the algorithm. The local density of each data point and the distance between the points with higher density were calculated. In addition, the cluster centers were selected based on the principle that the two indicators were higher. The noncentral points were classified and optimized, and the clustering result was then outputted. The normal data of clustering results were divided into several categories, and there exists some noise whose data points were significantly different from most of the normal data. A specific analysis was conducted for these abnormal data. An outlier detection algorithm was then used to process the original data, the cleaned abnormal data were extracted, and abnormal events such as excessive transit time, a short transit time, and a high load were detected. Finally, the anomalies in the data obtained using the isolated point detection method were compared with the anomalies in the data of the fast peak clustering algorithm. The results show that the accuracy of fast peak clustering used to identify anomalous events is higher than that of the isolated point detection method by nearly 20%, which verifies the validity and accuracy of the proposed algorithm. The method proposed in this paper can effectively and accurately identify hidden traffic jams such as road congestion, long stays, exit charges, and network equipment failure in the charging data, and provide theoretical support for operational services and management decisions for practical applications of an expressway. 3 tabs, 6 figs, 25 refs.
Last Update: 2018-10-23