Low-dimensional models for compression, estimation and prediction of large-scale traffic data
Intelligent Transportation Systems (ITS) often operate on large road networks and collect traffic data with high temporal resolution. The volume of the collected data severely limits the scalability of real-time traffic operations. We propose datadriven models that can help intelligent transportatio...
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| Format: | Thesis |
| Language: | English |
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2016
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| Online Access: | https://hdl.handle.net/10356/69423 |