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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Data Mining, ICDM |
| Pages | 141-150 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - 2012 |
| Externally published | Yes |
| Event | 12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels Duration: 10 Dec 2012 → 13 Dec 2012 |
Other
| Other | 12th IEEE International Conference on Data Mining, ICDM 2012 |
|---|---|
| City | Brussels |
| Period | 10/12/12 → 13/12/12 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Inferring the root cause in road traffic anomalies. / Chawla, Sanjay; Zheng, Yu; Hu, Jiafeng.
Proceedings - IEEE International Conference on Data Mining, ICDM. 2012. p. 141-150 6413908.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Inferring the root cause in road traffic anomalies
AU - Chawla, Sanjay
AU - Zheng, Yu
AU - Hu, Jiafeng
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84874085057&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2012.104
DO - 10.1109/ICDM.2012.104
M3 - Conference contribution
AN - SCOPUS:84874085057
SN - 9780769549057
SP - 141
EP - 150
BT - Proceedings - IEEE International Conference on Data Mining, ICDM
ER -