Year 2020,
Volume: 8 Issue: 1, 49 - 54, 30.06.2020
Ali Tahir Karaşahin
,
Abdullah Erdal Tümer
References
- 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
Year 2020,
Volume: 8 Issue: 1, 49 - 54, 30.06.2020
Ali Tahir Karaşahin
,
Abdullah Erdal Tümer
Abstract
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.
References
- 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.