TY - GEN
T1 - A new framework for traffic anomaly detection
AU - Lan, Jinsong
AU - Long, Cheng
AU - Wong, Raymond Chi Wing
AU - Chen, Youyang
AU - Fu, Yanjie
AU - Guo, Danhuai
AU - Liu, Shuguang
AU - Ge, Yong
AU - Zhou, Yuanchun
AU - Li, Jianhui
N1 - Funding Information:
We thank the support of Natural Science Foundation of China 91224006, 61003138 and 41371386, the Strategic Priority Research Program of the Chinese Academy of Sciences XDA06010202 and XDA05050601, 12th Five-Year Plan for Science & Technology Support 2012BAK17B01 and 2013BAD15B02. The research of Cheng Long and Raymond Chi-Wing Wong is supported by grant FS- GRF14EG34.
Publisher Copyright:
Copyright © SIAM.
PY - 2014
Y1 - 2014
N2 - Trajectory data is becoming more and more popular nowadays and extensive studies have been conducted on trajectory data. One important research direction about trajectory data is the anomaly detection which is to find all anomalies based on trajectory patterns in a road network. In this paper, we introduce a road segment-based anomaly detection problem, which is to detect the abnormal road segments each of which has its "real" traffic deviating from its "expected" traffic and to infer the major causes of anomalies on the road network. First, a deviation-based method is proposed to quantify the anomaly of reach road segment. Second, based on the observation that one anomaly from a road segment can trigger other anomalies from the road segments nearby, a diffusionbased method based on a heat diffusion model is proposed to infer the major causes of anomalies on the whole road network. To validate our methods, we conduct intensive experiments on a large real-world GPS dataset of about 23,000 taxis in Shenzhen, China to demonstrate the performance of our algorithms.
AB - Trajectory data is becoming more and more popular nowadays and extensive studies have been conducted on trajectory data. One important research direction about trajectory data is the anomaly detection which is to find all anomalies based on trajectory patterns in a road network. In this paper, we introduce a road segment-based anomaly detection problem, which is to detect the abnormal road segments each of which has its "real" traffic deviating from its "expected" traffic and to infer the major causes of anomalies on the road network. First, a deviation-based method is proposed to quantify the anomaly of reach road segment. Second, based on the observation that one anomaly from a road segment can trigger other anomalies from the road segments nearby, a diffusionbased method based on a heat diffusion model is proposed to infer the major causes of anomalies on the whole road network. To validate our methods, we conduct intensive experiments on a large real-world GPS dataset of about 23,000 taxis in Shenzhen, China to demonstrate the performance of our algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84947780051&partnerID=8YFLogxK
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U2 - 10.1137/1.9781611973440.100
DO - 10.1137/1.9781611973440.100
M3 - Conference contribution
AN - SCOPUS:84947780051
T3 - SIAM International Conference on Data Mining 2014, SDM 2014
SP - 875
EP - 883
BT - SIAM International Conference on Data Mining 2014, SDM 2014
A2 - Zaki, Mohammed
A2 - Obradovic, Zoran
A2 - Ning-Tan, Pang
A2 - Banerjee, Arindam
A2 - Kamath, Chandrika
A2 - Parthasarathy, Srinivasan
PB - Society for Industrial and Applied Mathematics Publications
T2 - 14th SIAM International Conference on Data Mining, SDM 2014
Y2 - 24 April 2014 through 26 April 2014
ER -