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OPTIMUM SIGNALIZATION ALGORITHM SUGGESTION WITH THE INTERSECTION DELAY OPTIMIZATION

Yıl 2023, Cilt: 5 Sayı: 2, 95 - 103, 30.06.2023

Abdülkadir Çıldır Mesud Kahriman Mesut Tigdemir

https://doi.org/10.47933/ijeir.1189981

Öz

In this study, a sample has been carried out in order to discharge the current vehicle flow in minimum time at the intersection. The sample has been carried out at an isolated and lossless four-legged intersection. An ideal traffic signaling algorithm have been presented by simultaneously calculating the minimum total intersection delay at this intersection. The suggested algorithm that is actuated method have been compared with classical and different actuated methods. It has been observed that the presented algorithm reduces the intersection delay by 65 % compared to the classical method and 51 % compared to some actuated methods.

Anahtar Kelimeler

Isolated and lossless intersection intersection delay actuated method optimum traffic signalization.

Kaynakça

  • [1] Cai, M., Yin, Y., Xie, M., (2009). Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transportation Research Part D: Transport Environment, 14, pp. 32-41.
  • [2] Sharma, A. R., Kharol, S. K., Badarinath, K., (2010). Influence of vehicular traffic on urban air quality–A case study of Hyderabad, India. Transportation Research Part D: Transport Environment, 15, pp. 154-159.
  • [3] Akanbi, L., Olajubu, E., (2012). A fuzzy-based intelligent traffic control system for managing VIP-induced chaos at road intersections. African Journal of Computing ICT, 5, pp. 109-119.
  • [4] Nuzzolo, A., Comi, A., (2016). Advanced public transport and intelligent transport systems: new modelling challenges. Transportmetrica A: Transport Science, 12, pp. 674-699.
  • [5] Ding, J., Zhang, Y., Li, L., (2018). Accessibility measure of bus transit networks. IET Intelligent Transport Systems, 12, pp. 682-688.
  • [6] Nie, Y. M., (2017). How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China. Transportation Research Part C: Emerging Technologies, 79, pp. 242-256.
  • [7] Dong, Y., Wang, S., Li, L., Zhang, Z., (2018). An empirical study on travel patterns of internet based ride-sharing. Transportation research part C: emerging technologies, 86, pp. 1-22.
  • [8] Wang, F.-Y., (2010). Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 11, pp. 630-638.
  • [9] Hamilton, A., Waterson, B., Cherrett, T., Robinson, A., Snell, I., (2013). The evolution of urban traffic control: changing policy and technology. Transportation planning technology, 36, pp. 24-43.
  • [10] Li, L., Wang, F.-Y., (2018). A review of past 100-yera and perspective of next 50-year development of ground traffic control. Autom. Sin. 44 (4), 101, pp. 577–583.
  • [11] Chen, H., Zuo, C., Yuan, Y., (2013), Control strategy research of engine smart start/stop system for a micro car. in SAE Technical Paper 2013-01-0585, Detroit Michigan, United States.
  • [12] Murat, Y. Ş., (2006). Sinyalize kavşaklardaki taşit gecikmelerinin bulanik mantik ile modellenmesi. Teknik Dergi, 17, pp. 3903-3916.
  • [13] Akgüngör, A. P., Doğan E., (2016). Bulanık mantık ile diğer sinyal denetim sistemlerinin karşılaştırılması: üç kollu sinyalize kavşak örneği. El-Cezeri Journal of Science and Engineering, 3, pp. 110-117.
  • [14] Mutlu E., Yavuz M. E., (2008). Bulanık Mantık ve Yapay Sinir Ağı ile Sinyalize Kavşaklardaki Taşıt Gecikmelerinin Modellenmesi. Elec Lett Sci Eng, 4, pp. 11-18.
  • [15] Zekri, D., Defude, B., Delot, T., (2010). Summarizing sensors data in vehicular ad hoc networks. RAIRO- Operations Research, 44, pp. 345-364.
  • [16] Ospina-Mateus, H., Quintana Jimenez, L. A., Lopez-Valdes, F. J., Sankar Sana, S., (2021). Prediction of motorcyclist traffic crashes in Cartagena (Colombia): development of a safety performance function. RAIRO-Operations Research, 55, pp. 1257 - 1278.
  • [17] Arel, I., C. Liu, T. Urbanik and A. Kohls, (2010). Reinforcement learning-based multi-agent system for network traffic signal control. IET Intelligent Transport Systems., 4, pp. 128-135.
  • [18] R. P. Roess, E. S. P., and W. R. McShane, (2004). Traffic Engineering. Pearson, NJ, USA: Prentice-Hall.
  • [19] Robertson, D. I., Bretherton, R. D., (1991). Optimizing networks of traffic signals in real time-the SCOOT method. IEEE Transactions on vehicular technology, 40, pp. 11-15.
  • [20] Mirchandani, P., Head, L., (2001). A real-time traffic signal control system: architecture, algorithms, and analysis. Transportation Research Part C: Emerging Technologies., 9, pp. 415-432.
  • [21] Ren, Y., Wang, Y., Yu, G., Liu, H., Xiao, L., (2016). An adaptive signal control scheme to prevent intersection traffic blockage. IEEE Transactions on Intelligent Transportation Systems, 18, pp. 1519-1528.
  • [22] Xu, B., Ban, X. J., Bian, Y., Li, W., Wang, J., Li, S. E., Li, K., (2018). Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections. IEEE Transactions on Intelligent Transportation Systems, 20, pp. 1390-1403.
  • [23] Akcelik, R., (1981). Traffic signals: capacity and timing analysis. ARRB Transport Research Ltd., Greythorn Victoria 3104, Australia
  • [24] Wachs, M., Samuels, J. M., Skinner, R. E., (2000). Highway capacity manual. TRB Business Office, United States of America.
  • [25] Webster, F. V., (1958). Traffic signal settings. Road Research Technical Paper, Road Research Laboratory, Her Majesty Stationary Office, London, UK.
  • [26] Li, D., Wu, J., Xu, M., Wang, Z., Hu, K., (2020). Adaptive traffic signal control model on intersections based on deep reinforcement learning. Journal of Advanced Transportation, 2020, pp. 1-14, DOI: 10.1155/2020/6505893.
  • [27] Zeng, J., Hu, J., Zhang, Y., "Adaptive traffic signal control with deep recurrent Q-learning." pp. 1215-1220.
  • [28] Chang, Y. L., Zhou, Y. Y., "Research of signalized intersection delay model by using optimization method." pp. 2742-2746.
  • [29] Li, Z., Elefteriadou, L., Ranka, S., (2014). Signal control optimization for automated vehicles at isolated signalized intersections. Transportation Research Part C: Emerging Technologies, 49, pp. 1-18.

kavşak gecikme optimizasyonu ile ideal sinyalizasyon algoritma önerisi

Yıl 2023, Cilt: 5 Sayı: 2, 95 - 103, 30.06.2023

Abdülkadir Çıldır Mesud Kahriman Mesut Tigdemir

https://doi.org/10.47933/ijeir.1189981

Öz

Bu çalışmada kavşakta mevcut araç akışının minimum sürede boşaltılabilmesi için bir örneklem yapılmıştır. Örnek izole ve kayıpsız dört ayaklı bir kavşakta gerçekleştirilmiştir. Bu kavşaktaki minimum toplam kavşak gecikmesi eş zamanlı olarak hesaplanarak ideal bir trafik sinyalizasyon algoritması sunulmuştur. Uyarmalı yöntem olan önerilen algoritma, klasik ve farklı uyarmalı yöntemlerle karşılaştırılmıştır. Sunulan algoritmanın kavşak gecikmesini klasik yönteme göre %65, bazı tahrikli yöntemlere göre ise %51 oranında azalttığı gözlemlenmiştir.

Anahtar Kelimeler

izole ve kayıpsız kavşak kavşak gecikmesi uyarmalı metot ideal trafik sinyalizasyonu

Kaynakça

  • [1] Cai, M., Yin, Y., Xie, M., (2009). Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transportation Research Part D: Transport Environment, 14, pp. 32-41.
  • [2] Sharma, A. R., Kharol, S. K., Badarinath, K., (2010). Influence of vehicular traffic on urban air quality–A case study of Hyderabad, India. Transportation Research Part D: Transport Environment, 15, pp. 154-159.
  • [3] Akanbi, L., Olajubu, E., (2012). A fuzzy-based intelligent traffic control system for managing VIP-induced chaos at road intersections. African Journal of Computing ICT, 5, pp. 109-119.
  • [4] Nuzzolo, A., Comi, A., (2016). Advanced public transport and intelligent transport systems: new modelling challenges. Transportmetrica A: Transport Science, 12, pp. 674-699.
  • [5] Ding, J., Zhang, Y., Li, L., (2018). Accessibility measure of bus transit networks. IET Intelligent Transport Systems, 12, pp. 682-688.
  • [6] Nie, Y. M., (2017). How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China. Transportation Research Part C: Emerging Technologies, 79, pp. 242-256.
  • [7] Dong, Y., Wang, S., Li, L., Zhang, Z., (2018). An empirical study on travel patterns of internet based ride-sharing. Transportation research part C: emerging technologies, 86, pp. 1-22.
  • [8] Wang, F.-Y., (2010). Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 11, pp. 630-638.
  • [9] Hamilton, A., Waterson, B., Cherrett, T., Robinson, A., Snell, I., (2013). The evolution of urban traffic control: changing policy and technology. Transportation planning technology, 36, pp. 24-43.
  • [10] Li, L., Wang, F.-Y., (2018). A review of past 100-yera and perspective of next 50-year development of ground traffic control. Autom. Sin. 44 (4), 101, pp. 577–583.
  • [11] Chen, H., Zuo, C., Yuan, Y., (2013), Control strategy research of engine smart start/stop system for a micro car. in SAE Technical Paper 2013-01-0585, Detroit Michigan, United States.
  • [12] Murat, Y. Ş., (2006). Sinyalize kavşaklardaki taşit gecikmelerinin bulanik mantik ile modellenmesi. Teknik Dergi, 17, pp. 3903-3916.
  • [13] Akgüngör, A. P., Doğan E., (2016). Bulanık mantık ile diğer sinyal denetim sistemlerinin karşılaştırılması: üç kollu sinyalize kavşak örneği. El-Cezeri Journal of Science and Engineering, 3, pp. 110-117.
  • [14] Mutlu E., Yavuz M. E., (2008). Bulanık Mantık ve Yapay Sinir Ağı ile Sinyalize Kavşaklardaki Taşıt Gecikmelerinin Modellenmesi. Elec Lett Sci Eng, 4, pp. 11-18.
  • [15] Zekri, D., Defude, B., Delot, T., (2010). Summarizing sensors data in vehicular ad hoc networks. RAIRO- Operations Research, 44, pp. 345-364.
  • [16] Ospina-Mateus, H., Quintana Jimenez, L. A., Lopez-Valdes, F. J., Sankar Sana, S., (2021). Prediction of motorcyclist traffic crashes in Cartagena (Colombia): development of a safety performance function. RAIRO-Operations Research, 55, pp. 1257 - 1278.
  • [17] Arel, I., C. Liu, T. Urbanik and A. Kohls, (2010). Reinforcement learning-based multi-agent system for network traffic signal control. IET Intelligent Transport Systems., 4, pp. 128-135.
  • [18] R. P. Roess, E. S. P., and W. R. McShane, (2004). Traffic Engineering. Pearson, NJ, USA: Prentice-Hall.
  • [19] Robertson, D. I., Bretherton, R. D., (1991). Optimizing networks of traffic signals in real time-the SCOOT method. IEEE Transactions on vehicular technology, 40, pp. 11-15.
  • [20] Mirchandani, P., Head, L., (2001). A real-time traffic signal control system: architecture, algorithms, and analysis. Transportation Research Part C: Emerging Technologies., 9, pp. 415-432.
  • [21] Ren, Y., Wang, Y., Yu, G., Liu, H., Xiao, L., (2016). An adaptive signal control scheme to prevent intersection traffic blockage. IEEE Transactions on Intelligent Transportation Systems, 18, pp. 1519-1528.
  • [22] Xu, B., Ban, X. J., Bian, Y., Li, W., Wang, J., Li, S. E., Li, K., (2018). Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections. IEEE Transactions on Intelligent Transportation Systems, 20, pp. 1390-1403.
  • [23] Akcelik, R., (1981). Traffic signals: capacity and timing analysis. ARRB Transport Research Ltd., Greythorn Victoria 3104, Australia
  • [24] Wachs, M., Samuels, J. M., Skinner, R. E., (2000). Highway capacity manual. TRB Business Office, United States of America.
  • [25] Webster, F. V., (1958). Traffic signal settings. Road Research Technical Paper, Road Research Laboratory, Her Majesty Stationary Office, London, UK.
  • [26] Li, D., Wu, J., Xu, M., Wang, Z., Hu, K., (2020). Adaptive traffic signal control model on intersections based on deep reinforcement learning. Journal of Advanced Transportation, 2020, pp. 1-14, DOI: 10.1155/2020/6505893.
  • [27] Zeng, J., Hu, J., Zhang, Y., "Adaptive traffic signal control with deep recurrent Q-learning." pp. 1215-1220.
  • [28] Chang, Y. L., Zhou, Y. Y., "Research of signalized intersection delay model by using optimization method." pp. 2742-2746.
  • [29] Li, Z., Elefteriadou, L., Ranka, S., (2014). Signal control optimization for automated vehicles at isolated signalized intersections. Transportation Research Part C: Emerging Technologies, 49, pp. 1-18.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Abdülkadir Çıldır 0000-0003-1789-6088

Mesud Kahriman 0000-0003-0731-0936

Mesut Tigdemir 0000-0002-5303-2722

Erken Görünüm Tarihi 6 Haziran 2023
Yayımlanma Tarihi 30 Haziran 2023
Kabul Tarihi 21 Şubat 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 5 Sayı: 2

Kaynak Göster

APA Çıldır, A., Kahriman, M., & Tigdemir, M. (2023). OPTIMUM SIGNALIZATION ALGORITHM SUGGESTION WITH THE INTERSECTION DELAY OPTIMIZATION. International Journal of Engineering and Innovative Research, 5(2), 95-103. https://doi.org/10.47933/ijeir.1189981
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