Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 8 Sayı: 1, 49 - 54, 30.06.2020

Öz

Kaynakça

  • Altun, İ., Dündar, S., & Yöntem, K. (2005). Yapay Sinir Ağlari İle Trafik Akim Kontrolü. Deprem Sempozyumu, Kocaeli, 1335-1344.
  • Babu, K. R. M. (2018). IOT for ITS: An IOT Based Dynamic Traffic Signal Control. (Ed.),^(Eds.). 2018 International Conference on Inventive Research in Computing Applications (ICIRCA).
  • Day, C. M., Li, H., Richardson, L. M., Howard, J., Platte, T., Sturdevant, J. R., & Bullock, D. M. (2017). Detector-free optimization of traffic signal offsets with connected vehicle data. Transportation Research Record, 2620(1), 54-68.
  • Dogan, E., Payidar Akgungor, A., & Arslan, T. (2016). Estimation of delay and vehicle stops at signalized intersections using artificial neural network. Engineering Review: Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike, računarstva i građevinarstva, 36(2), 157-165.
  • Dougherty, M. (1995). A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies, 3(4), 247-260.
  • Ergün, S., & Aydoğan, T. (2013). Kavşak Sinyalizasyon Sisteminin JACK Etmen Geliştirme Platformunun Kullanılarak Oluşturulması. Bilişim Teknolojileri Dergisi, 6(1), 816.
  • Guler, S. I., Menendez, M., & Meier, L. (2014). Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C: Emerging Technologies, 46, 121-131.
  • Jacobson, L. (2013). Introduction to Artificial Neural Networks. The Project Spot, 5.
  • Kiyildi, R. K. (2017, September). Türkiye için Yapay Sinir Ağları Yöntemi ile Trafik Kazası Tahmini Araştırması. In 5th International Symposium on Innovative Technologies in Engineering and Science 29-30 September 2017 (ISITES2017 Baku-Azerbaijan).
  • Li, L., & Wen, D. (2015). Parallel systems for traffic control: A rethinking. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1179-1182.
  • Liu, H. X., Wu, X., Ma, W., & Hu, H. (2009). Real-time queue length estimation for congested signalized intersections. Transportation Research Part C: Emerging Technologies, 17(4), 412-427.
  • Murat, Y. Ş., & Başkan, Ö. (2006). İzole Sinyalize Kavşaklardaki Ortalama Taşit Gecikmelerinin Yapay Sinir Ağlari İle Modellenmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 12, 214-227.
  • Nason, N. (2005). TRAFFIC SAFETY FACTS 2005-A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System, National Highway Traffic Safety Administration. National Center for Statistics and Analysis, US Department of Transportation, Washington, DC, 20590.
  • Rodegerdts, L. A., Nevers, B. L., Robinson, B., Ringert, J., Koonce, P., Bansen, J., Nguyen, T., McGill, J., Stewart, D., & Suggett, J. (2004). Signalized intersections: informational guide (Saito, M., & Fan, J. (1999). Multilayer artificial neural networks for level-of-service analysis of signalized intersections. Transportation Research Record, 1678(1), 216-224.
  • Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies, 71, 143-163.
  • Tektaş, M., Akbaş, A., & Topuz, V. (2002). Yapay zeka tekniklerinin trafik kontrolünde kullanilmasi üzerine bir inceleme.
  • Timotheou, S., Panayiotou, C. G., & Polycarpou, M. M. (2014). Distributed traffic signal control using the cell transmission model via the alternating direction method of multipliers. IEEE Transactions on Intelligent Transportation Systems, 16(2), 919-933.
  • Xie, X.-F., Smith, S. F., Lu, L., & Barlow, G. J. (2012). Schedule-driven intersection control. Transportation Research Part C: Emerging Technologies, 24, 168-189.
  • Zhou, Z., De Schutter, B., Lin, S., & Xi, Y. (2016). Two-level hierarchical model-based predictive control for large-scale urban traffic networks. IEEE Transactions on Control Systems Technology, 25(2), 496-508.
  • Zhu, F., & Ukkusuri, S. V. (2015). A linear programming formulation for autonomous intersection control within a dynamic traffic assignment and connected vehicle environment. Transportation Research Part C: Emerging Technologies, 55, 363-378.

Real time traffic signal timing approach based on artificial neural network

Yıl 2020, Cilt: 8 Sayı: 1, 49 - 54, 30.06.2020

Öz

As the population increases, is more and more increasing the number of vehicles in cities. The increasing number of vehicle make traffic management complicated. Difficult traffic management leads to more fuel consumption, CO2 and other harmful emissions. Therefore, real-time optimization of traffic lights (signaling) used in traffic management can make traffic management more efficient. In this study, green light time is optimized by estimating the number of vehicles in an intersection with signal lights in Konya city center through artificial neural network. The results are evaluated with different performance criteria and it has been shown that the developed estimation model can be successfully used to optimize the green light durations.

Kaynakça

  • Altun, İ., Dündar, S., & Yöntem, K. (2005). Yapay Sinir Ağlari İle Trafik Akim Kontrolü. Deprem Sempozyumu, Kocaeli, 1335-1344.
  • Babu, K. R. M. (2018). IOT for ITS: An IOT Based Dynamic Traffic Signal Control. (Ed.),^(Eds.). 2018 International Conference on Inventive Research in Computing Applications (ICIRCA).
  • Day, C. M., Li, H., Richardson, L. M., Howard, J., Platte, T., Sturdevant, J. R., & Bullock, D. M. (2017). Detector-free optimization of traffic signal offsets with connected vehicle data. Transportation Research Record, 2620(1), 54-68.
  • Dogan, E., Payidar Akgungor, A., & Arslan, T. (2016). Estimation of delay and vehicle stops at signalized intersections using artificial neural network. Engineering Review: Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike, računarstva i građevinarstva, 36(2), 157-165.
  • Dougherty, M. (1995). A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies, 3(4), 247-260.
  • Ergün, S., & Aydoğan, T. (2013). Kavşak Sinyalizasyon Sisteminin JACK Etmen Geliştirme Platformunun Kullanılarak Oluşturulması. Bilişim Teknolojileri Dergisi, 6(1), 816.
  • Guler, S. I., Menendez, M., & Meier, L. (2014). Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C: Emerging Technologies, 46, 121-131.
  • Jacobson, L. (2013). Introduction to Artificial Neural Networks. The Project Spot, 5.
  • Kiyildi, R. K. (2017, September). Türkiye için Yapay Sinir Ağları Yöntemi ile Trafik Kazası Tahmini Araştırması. In 5th International Symposium on Innovative Technologies in Engineering and Science 29-30 September 2017 (ISITES2017 Baku-Azerbaijan).
  • Li, L., & Wen, D. (2015). Parallel systems for traffic control: A rethinking. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1179-1182.
  • Liu, H. X., Wu, X., Ma, W., & Hu, H. (2009). Real-time queue length estimation for congested signalized intersections. Transportation Research Part C: Emerging Technologies, 17(4), 412-427.
  • Murat, Y. Ş., & Başkan, Ö. (2006). İzole Sinyalize Kavşaklardaki Ortalama Taşit Gecikmelerinin Yapay Sinir Ağlari İle Modellenmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 12, 214-227.
  • Nason, N. (2005). TRAFFIC SAFETY FACTS 2005-A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System, National Highway Traffic Safety Administration. National Center for Statistics and Analysis, US Department of Transportation, Washington, DC, 20590.
  • Rodegerdts, L. A., Nevers, B. L., Robinson, B., Ringert, J., Koonce, P., Bansen, J., Nguyen, T., McGill, J., Stewart, D., & Suggett, J. (2004). Signalized intersections: informational guide (Saito, M., & Fan, J. (1999). Multilayer artificial neural networks for level-of-service analysis of signalized intersections. Transportation Research Record, 1678(1), 216-224.
  • Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies, 71, 143-163.
  • Tektaş, M., Akbaş, A., & Topuz, V. (2002). Yapay zeka tekniklerinin trafik kontrolünde kullanilmasi üzerine bir inceleme.
  • Timotheou, S., Panayiotou, C. G., & Polycarpou, M. M. (2014). Distributed traffic signal control using the cell transmission model via the alternating direction method of multipliers. IEEE Transactions on Intelligent Transportation Systems, 16(2), 919-933.
  • Xie, X.-F., Smith, S. F., Lu, L., & Barlow, G. J. (2012). Schedule-driven intersection control. Transportation Research Part C: Emerging Technologies, 24, 168-189.
  • Zhou, Z., De Schutter, B., Lin, S., & Xi, Y. (2016). Two-level hierarchical model-based predictive control for large-scale urban traffic networks. IEEE Transactions on Control Systems Technology, 25(2), 496-508.
  • Zhu, F., & Ukkusuri, S. V. (2015). A linear programming formulation for autonomous intersection control within a dynamic traffic assignment and connected vehicle environment. Transportation Research Part C: Emerging Technologies, 55, 363-378.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Ali Tahir Karaşahin 0000-0002-7440-1312

Abdullah Erdal Tümer 0000-0001-7747-9441

Yayımlanma Tarihi 30 Haziran 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 1

Kaynak Göster

APA Karaşahin, A. T., & Tümer, A. E. (2020). Real time traffic signal timing approach based on artificial neural network. MANAS Journal of Engineering, 8(1), 49-54.
AMA Karaşahin AT, Tümer AE. Real time traffic signal timing approach based on artificial neural network. MJEN. Haziran 2020;8(1):49-54.
Chicago Karaşahin, Ali Tahir, ve Abdullah Erdal Tümer. “Real Time Traffic Signal Timing Approach Based on Artificial Neural Network”. MANAS Journal of Engineering 8, sy. 1 (Haziran 2020): 49-54.
EndNote Karaşahin AT, Tümer AE (01 Haziran 2020) Real time traffic signal timing approach based on artificial neural network. MANAS Journal of Engineering 8 1 49–54.
IEEE A. T. Karaşahin ve A. E. Tümer, “Real time traffic signal timing approach based on artificial neural network”, MJEN, c. 8, sy. 1, ss. 49–54, 2020.
ISNAD Karaşahin, Ali Tahir - Tümer, Abdullah Erdal. “Real Time Traffic Signal Timing Approach Based on Artificial Neural Network”. MANAS Journal of Engineering 8/1 (Haziran 2020), 49-54.
JAMA Karaşahin AT, Tümer AE. Real time traffic signal timing approach based on artificial neural network. MJEN. 2020;8:49–54.
MLA Karaşahin, Ali Tahir ve Abdullah Erdal Tümer. “Real Time Traffic Signal Timing Approach Based on Artificial Neural Network”. MANAS Journal of Engineering, c. 8, sy. 1, 2020, ss. 49-54.
Vancouver Karaşahin AT, Tümer AE. Real time traffic signal timing approach based on artificial neural network. MJEN. 2020;8(1):49-54.

Manas Journal of Engineering 

16155