Feature selection method for traffic prediction
Short-term traffic prediction has been one of the most essential parts of most Intelligent Transportation Systems (ITS). As a main target in traffic prediction, improving the prediction accuracy has always been the research focus. Feature selection provides an optimal pre-processing step in traff...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/68665 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Short-term traffic prediction has been one of the most essential parts of most
Intelligent Transportation Systems (ITS). As a main target in traffic prediction,
improving the prediction accuracy has always been the research focus. Feature
selection provides an optimal pre-processing step in traffic prediction. In this
dissertation report, several pre-processing steps were investigated and applied, such as
finding spatial features, extracting links data, using Local Fisher Discriminant
Analysis (LFDA), etc. After the feature selection, Support Vector Machine (SVM)
was introduced on the new data space for traffic forecasting. Some experiments were
conducted on the dataset of Singapore transportation. And the experimental results
have shown the effectiveness and efficiency of the combined prediction strategy. |
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