inferring the root cause in road traffic anomalies

ISCAS OpenIR

	inferring the root cause in road traffic anomalies
	Chawla Sanjay; Zheng Yu; Hu Jiafeng
	2012
Conference Name	12th IEEE International Conference on Data Mining, ICDM 2012
Source	Proceedings - IEEE International Conference on Data Mining, ICDM
Pages	141-150
Conference Date	December 10, 2012 - December 13, 2012
Conference Place	Brussels, Belgium
Indexed Type	EI
ISSN	1550-4786
ISBN	9780769549057
Department	(1) School of Information Technologies University of Sydney Sydney Australia; (2) Microsoft Research Asia Beijing China; (3) Institute of Software Chinese Academy of Sciences Beijing China
English Abstract	We propose a novel two-step mining and optimization framework for inferring the root cause of anomalies that appear in road traffic data. We model road traffic as a time-dependent flow on a network formed by partitioning a city into regions bounded by major roads. In the first step we identify link anomalies based on their deviation from their historical traffic profile. However, link anomalies on their own shed very little light on what caused them to be anomalous. In the second step we take a generative approach by modeling the flow in a network in terms of the origin-destination (OD) matrix which physically relates the latent flow between origin and destination and the observable flow on the links. The key insight is that instead of using all of link traffic as the observable vector we only use the link anomaly vector. By solving an L 1 inverse problem we infer the routes (the origin-destination pairs) which gave rise to the link anomalies. Experiments on a very large GPS data set consisting on nearly eight hundred million data points demonstrate that we can discover routes which can clearly explain the appearance of link anomalies. The use of optimization techniques to explain observable anomalies in a generative fashion is, to the best of our knowledge, entirely novel. © 2012 IEEE.; We propose a novel two-step mining and optimization framework for inferring the root cause of anomalies that appear in road traffic data. We model road traffic as a time-dependent flow on a network formed by partitioning a city into regions bounded by major roads. In the first step we identify link anomalies based on their deviation from their historical traffic profile. However, link anomalies on their own shed very little light on what caused them to be anomalous. In the second step we take a generative approach by modeling the flow in a network in terms of the origin-destination (OD) matrix which physically relates the latent flow between origin and destination and the observable flow on the links. The key insight is that instead of using all of link traffic as the observable vector we only use the link anomaly vector. By solving an L 1 inverse problem we infer the routes (the origin-destination pairs) which gave rise to the link anomalies. Experiments on a very large GPS data set consisting on nearly eight hundred million data points demonstrate that we can discover routes which can clearly explain the appearance of link anomalies. The use of optimization techniques to explain observable anomalies in a generative fashion is, to the best of our knowledge, entirely novel. © 2012 IEEE.
Keyword	Data Mining Inverse Problems
Language	英语
Content Type	会议论文
URI	http://ir.iscas.ac.cn/handle/311060/15914
Collection	中国科学院软件研究所
Recommended Citation GB/T 7714	Chawla Sanjay,Zheng Yu,Hu Jiafeng. inferring the root cause in road traffic anomalies[C],2012:141-150.

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