Real time traffic signal timing approach based on artificial neural network
| dc.contributor.author | Karaşahin, Ali Tahir | |
| dc.contributor.author | Tümer, Abdullah Erdal | |
| dc.date.accessioned | 2024-09-29T16:33:17Z | |
| dc.date.available | 2024-09-29T16:33:17Z | |
| dc.date.issued | 2020 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description.abstract | As the population increases, is more and more increasing the number of vehicles in cities. Theincreasing number of vehicle make traffic management complicated. Difficult trafficmanagement leads to more fuel consumption, CO2 and other harmful emissions. Therefore, realtime optimization of traffic lights (signaling) used in traffic management can make trafficmanagement more efficient. In this study, green light time is optimized by estimating thenumber of vehicles in an intersection with signal lights in Konya city center through artificialneural network. The results are evaluated with different performance criteria and it has beenshown that the developed estimation model can be successfully used to optimize the green lightdurations. | en_US |
| dc.identifier.endpage | 54 | en_US |
| dc.identifier.issue | 1 | en_US |
| dc.identifier.startpage | 49 | en_US |
| dc.identifier.trdizinid | 358608 | en_US |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/358608 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/11722 | |
| dc.identifier.volume | 8 | en_US |
| dc.indekslendigikaynak | TR-Dizin | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | Manas Journal of Engineering | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.title | Real time traffic signal timing approach based on artificial neural network | en_US |
| dc.type | Article | en_US |
