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Title:
inferring the root cause in road traffic anomalies
Author: Chawla Sanjay ; Zheng Yu ; Hu Jiafeng
Source: Proceedings - IEEE International Conference on Data Mining, ICDM
Conference Name: 12th IEEE International Conference on Data Mining, ICDM 2012
Conference Date: December 10, 2012 - December 13, 2012
Issued Date: 2012
Conference Place: Brussels, Belgium
Keyword: Data mining ; Inverse problems
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
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.
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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15914
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Chawla Sanjay,Zheng Yu,Hu Jiafeng. inferring the root cause in road traffic anomalies[C]. 见:12th IEEE International Conference on Data Mining, ICDM 2012. Brussels, Belgium. December 10, 2012 - December 13, 2012.
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