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Vehicle Classification For Automatic Traffic Density Estimation
Automatic traffic light control at intersection has recently become one of the most active research areas related to the development of intelligent transportation systems (ITS). Due to the massive growth in urbanization and traffic congestion, intelligent vision based traffic light controller is nee...
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| Format: | Thesis |
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AUC Knowledge Fountain
2014
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| _version_ | 1867613416467726336 |
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| access_status_str | Open Access |
| author | Abdelhady, Aya |
| author_browse | Abdelhady, Aya |
| author_facet | Abdelhady, Aya |
| author_sort | Abdelhady, Aya |
| collection | Thesis |
| dc_rights_str_mv | The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy. |
| description | Automatic traffic light control at intersection has recently become one of the most active research areas related to the development of intelligent transportation systems (ITS). Due to the massive growth in urbanization and traffic congestion, intelligent vision based traffic light controller is needed to reduce the traffi c delay and travel time especially in developing countries as the current automatic time based control is not realistic while sensor-based tra ffic light controller is not reliable in developing countries. Vision based traffi c light controller depends mainly on traffic congestion estimation at cross roads, because the main road junctions of a city are these roads where most of the road-beds are lost. Most of the previous studies related to this topic do not take unattended vehicles into consideration when estimating the tra ffic density or traffi c flow. In this study we would like to improve the performance of vision based traffi c light control by detecting stationary and unattended vehicles to give them higher weights, using image processing and pattern recognition techniques for much e ffective and e ffecient tra ffic congestion estimation. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-2205 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:47.730Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-2205 Vehicle Classification For Automatic Traffic Density Estimation Abdelhady, Aya Automatic traffic light control at intersection has recently become one of the most active research areas related to the development of intelligent transportation systems (ITS). Due to the massive growth in urbanization and traffic congestion, intelligent vision based traffic light controller is needed to reduce the traffi c delay and travel time especially in developing countries as the current automatic time based control is not realistic while sensor-based tra ffic light controller is not reliable in developing countries. Vision based traffi c light controller depends mainly on traffic congestion estimation at cross roads, because the main road junctions of a city are these roads where most of the road-beds are lost. Most of the previous studies related to this topic do not take unattended vehicles into consideration when estimating the tra ffic density or traffi c flow. In this study we would like to improve the performance of vision based traffi c light control by detecting stationary and unattended vehicles to give them higher weights, using image processing and pattern recognition techniques for much e ffective and e ffecient tra ffic congestion estimation. 2014-02-01T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/1206 https://fount.aucegypt.edu/context/etds/article/2205/viewcontent/Aya_Salama_Thesis_updated.pdf The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy. Theses and Dissertations AUC Knowledge Fountain Electric lighting--Control Traffic monitoring |
| spellingShingle | Electric lighting--Control Traffic monitoring Abdelhady, Aya Vehicle Classification For Automatic Traffic Density Estimation |
| title | Vehicle Classification For Automatic Traffic Density Estimation |
| title_full | Vehicle Classification For Automatic Traffic Density Estimation |
| title_fullStr | Vehicle Classification For Automatic Traffic Density Estimation |
| title_full_unstemmed | Vehicle Classification For Automatic Traffic Density Estimation |
| title_short | Vehicle Classification For Automatic Traffic Density Estimation |
| title_sort | vehicle classification for automatic traffic density estimation |
| topic | Electric lighting--Control Traffic monitoring |
| url | https://fount.aucegypt.edu/etds/1206 https://fount.aucegypt.edu/context/etds/article/2205/viewcontent/Aya_Salama_Thesis_updated.pdf |
| work_keys_str_mv | AT abdelhadyaya vehicleclassificationforautomatictrafficdensityestimation |