Research Article

Real time traffic signal timing approach based on artificial neural network

Volume: 8 Number: 1 June 30, 2020
EN

Real time traffic signal timing approach based on artificial neural network

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.

Keywords

Dynamic traffic control,artificial neural network,traffic signalization,optimization

References

  1. Altun, İ., Dündar, S., & Yöntem, K. (2005). Yapay Sinir Ağlari İle Trafik Akim Kontrolü. Deprem Sempozyumu, Kocaeli, 1335-1344.
  2. 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).
  3. 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.
  4. 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.
  5. Dougherty, M. (1995). A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies, 3(4), 247-260.
  6. Ergün, S., & Aydoğan, T. (2013). Kavşak Sinyalizasyon Sisteminin JACK Etmen Geliştirme Platformunun Kullanılarak Oluşturulması. Bilişim Teknolojileri Dergisi, 6(1), 816.
  7. 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.
  8. Jacobson, L. (2013). Introduction to Artificial Neural Networks. The Project Spot, 5.
  9. 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).
  10. Li, L., & Wen, D. (2015). Parallel systems for traffic control: A rethinking. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1179-1182.
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. https://izlik.org/JA49PX88NF
AMA
1.Karaşahin AT, Tümer AE. Real time traffic signal timing approach based on artificial neural network. MJEN. 2020;8(1):49-54. https://izlik.org/JA49PX88NF
Chicago
Karaşahin, Ali Tahir, and Abdullah Erdal Tümer. 2020. “Real Time Traffic Signal Timing Approach Based on Artificial Neural Network”. MANAS Journal of Engineering 8 (1): 49-54. https://izlik.org/JA49PX88NF.
EndNote
Karaşahin AT, Tümer AE (June 1, 2020) Real time traffic signal timing approach based on artificial neural network. MANAS Journal of Engineering 8 1 49–54.
IEEE
[1]A. T. Karaşahin and A. E. Tümer, “Real time traffic signal timing approach based on artificial neural network”, MJEN, vol. 8, no. 1, pp. 49–54, June 2020, [Online]. Available: https://izlik.org/JA49PX88NF
ISNAD
Karaşahin, Ali Tahir - Tümer, Abdullah Erdal. “Real Time Traffic Signal Timing Approach Based on Artificial Neural Network”. MANAS Journal of Engineering 8/1 (June 1, 2020): 49-54. https://izlik.org/JA49PX88NF.
JAMA
1.Karaşahin AT, Tümer AE. Real time traffic signal timing approach based on artificial neural network. MJEN. 2020;8:49–54.
MLA
Karaşahin, Ali Tahir, and Abdullah Erdal Tümer. “Real Time Traffic Signal Timing Approach Based on Artificial Neural Network”. MANAS Journal of Engineering, vol. 8, no. 1, June 2020, pp. 49-54, https://izlik.org/JA49PX88NF.
Vancouver
1.Ali Tahir Karaşahin, Abdullah Erdal Tümer. Real time traffic signal timing approach based on artificial neural network. MJEN [Internet]. 2020 Jun. 1;8(1):49-54. Available from: https://izlik.org/JA49PX88NF