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About Transportation Research Part C: Nicholas G. Polson, Vadim O. Sokolov
This document summarizes a research paper that develops a deep learning model to predict short-term traffic flows. The model combines a sparse linear model fitted with L1 regularization as the first layer with subsequent tanh layers to capture nonlinear spatiotemporal effects. The model is shown to accurately predict traffic flows during two events with sudden changes: a Chicago Bears football game and an extreme snowstorm. Deep learning is able to incorporate multiple data sources and provide accurate 40-minute traffic flow forecasts under both recurrent and non-recurrent congestion conditions.
Detailed Information
| Author: | ['SNK'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 17 |
| Language: | English |
| Format: | |
| Price: | FREE |
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