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| Content Provider | ACM Digital Library |
|---|---|
| Author | Feng, Zhuonan Jin, Li Feng, Ling |
| Abstract | Urban black hole, as a traffic anomaly, has caused lots of catastrophic accidents in many big cities nowadays. Traditional methods only depend on the single source data (e.g., taxi trajectories) to design blackhole detection algorithm from one point of view, which is rather incomplete to describe the regional crowd flow. In this paper, we model the urban black holes in each region of New York City (NYC) at different time intervals with a 3-dimensional tensor by fusing cross-domain data sources. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we leverage the knowledge from geographical features, 311 complaint features and human mobility features to recover the blackhole situation throughout NYC. The information can facilitate local residents and officials' decision making. We evaluate our model with five datasets related to NYC, diagnosing the urban black holes that cannot be identified (or earlier than those detected) by a single dataset. Experimental results demonstrate the advantages beyond four baseline methods. |
| Starting Page | 2137 |
| Ending Page | 2142 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450340731 |
| DOI | 10.1145/2983323.2983655 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-10-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Tensor decomposition Urban black holes Cross-domain |
| Content Type | Text |
| Resource Type | Article |
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