Towards improved traffic predictions by incorporating rainfall forecasts
Weather conditions tend to have measurable impact on traffic conditions of the roads. This relationship is commonly studied at the network level without explicit explanation of the link performances. Furthermore, existing studies typically use high resolution traffic data which may not be available...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/10356/63892 |
| _version_ | 1826121710302134272 |
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| author | Ho, Victor Yao Tong |
| author2 | Justin Dauwels |
| author_facet | Justin Dauwels Ho, Victor Yao Tong |
| author_sort | Ho, Victor Yao Tong |
| collection | NTU |
| description | Weather conditions tend to have measurable impact on traffic conditions of the roads. This relationship is commonly studied at the network level without explicit explanation of the link performances. Furthermore, existing studies typically use high resolution traffic data which may not be available across the entire network and especially during the adverse weather conditions. In this project the impact of rainfall intensity is being explored on low-resolution speed band data. This additional information is being tested whether rainfall may improve the prediction accuracy of data-driven models for individual roads. To do so, the information about the rainfall intensity is incorporated into support vector machine (SVM) prediction algorithm. As a benchmark, only temporal features will be considered to predict near future traffic conditions during rainy weather. Numerical results for 616 road segments in Singapore confirm that rainfall impacts traffic conditions in terms of decreasing the driving speed. This reduction increases with the rain intensity. Furthermore, the results show that additional rainfall data enhances the prediction accuracy for certain number of links; while for the others the rainfall information is not that useful. |
| first_indexed | 2024-10-01T05:36:58Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/63892 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:36:58Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/638922023-07-07T16:44:52Z Towards improved traffic predictions by incorporating rainfall forecasts Ho, Victor Yao Tong Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Weather conditions tend to have measurable impact on traffic conditions of the roads. This relationship is commonly studied at the network level without explicit explanation of the link performances. Furthermore, existing studies typically use high resolution traffic data which may not be available across the entire network and especially during the adverse weather conditions. In this project the impact of rainfall intensity is being explored on low-resolution speed band data. This additional information is being tested whether rainfall may improve the prediction accuracy of data-driven models for individual roads. To do so, the information about the rainfall intensity is incorporated into support vector machine (SVM) prediction algorithm. As a benchmark, only temporal features will be considered to predict near future traffic conditions during rainy weather. Numerical results for 616 road segments in Singapore confirm that rainfall impacts traffic conditions in terms of decreasing the driving speed. This reduction increases with the rain intensity. Furthermore, the results show that additional rainfall data enhances the prediction accuracy for certain number of links; while for the others the rainfall information is not that useful. Bachelor of Engineering 2015-05-20T01:43:42Z 2015-05-20T01:43:42Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/63892 en Nanyang Technological University 52 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Ho, Victor Yao Tong Towards improved traffic predictions by incorporating rainfall forecasts |
| title | Towards improved traffic predictions by incorporating rainfall forecasts |
| title_full | Towards improved traffic predictions by incorporating rainfall forecasts |
| title_fullStr | Towards improved traffic predictions by incorporating rainfall forecasts |
| title_full_unstemmed | Towards improved traffic predictions by incorporating rainfall forecasts |
| title_short | Towards improved traffic predictions by incorporating rainfall forecasts |
| title_sort | towards improved traffic predictions by incorporating rainfall forecasts |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/63892 |
| work_keys_str_mv | AT hovictoryaotong towardsimprovedtrafficpredictionsbyincorporatingrainfallforecasts |