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A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

Yıl 2024, Cilt: 13 Sayı: 1, 292 - 306, 24.03.2024
https://doi.org/10.17798/bitlisfen.1388486

Öz

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

Kaynakça

  • [1] B. Vatchova, Y. Boneva, and A. Gegov, "Modelling and Simulation of Traffic Light Control," Cybernetics and Information Technologies, vol. 23, no. 3, pp. 179-191, 2023, doi: 10.2478/cait-2023-0032.
  • [2] D. Jutury, N. Kumar, A. Sachan, Y. Daultani, and N. Dhakad, "Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport network," Applied Intelligence, vol. 53, no. 6, pp. 7132-7153, 2023, doi: 10.1007/s10489-022-03827-3.
  • [3] A. M. George, V. I. George, and M. A. George, "IOT based Smart Traffic Light Control System," in 2018 International Conference on Control, Power, Communication and Computing Technologies, ICCPCCT 2018, 2018, pp. 148-151, doi: 10.1109/ICCPCCT.2018.8574285. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060024823&doi=10.1109%2fICCPCCT.2018.8574285&partnerID=40&md5=c045a62856fde8f2d6192d92beb1c808
  • [4] S. B. Walukow, F. J. Doringin, R. E. Katuuk, and A. S. Wauran, "Regulation of the Real Time Traffic Light at Teling Intersection in Manado City by using Fuzzy Logic and ANFIS," in 2018 International Conference on Applied Science and Technology (iCAST), 2018: IEEE, pp. 259-262.
  • [5] K. M. Udofia, J. O. Emagbetere, and F. O. Edeko, "Dynamic traffic signal phase sequencing for an isolated intersection using ANFIS," Automation, Control and Intelligent Systems, vol. 2, no. 2, pp. 21-26, 2014.
  • [6] S. Araghi, A. Khosravi, and D. Creighton, "Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network," Expert Systems with Applications, vol. 42, no. 9, pp. 4422-4431, 2015, doi: 10.1016/j.eswa.2015.01.063.
  • [7] S. Araghi, A. Khosravi, and D. Creighton, "Comparing the performance of different types of distributed fuzzy-based traffic signal controllers," Journal of Intelligent and Fuzzy Systems, vol. 36, no. 6, pp. 6155-6166, 2019, doi: 10.3233/JIFS-181993.
  • [8] U. Andayani et al., "Simulation of Dynamic Traffic Light Setting Using Adaptive Neuro-Fuzzy Inference System (ANFIS)," in Journal of Physics: Conference Series, 2019, vol. 1235, 1 ed., doi: 10.1088/1742-6596/1235/1/012058. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070022009&doi=10.1088%2f1742- 6596%2f1235%2f1%2f012058&partnerID=40&md5=ab0dba18e53025008af8a2cfe6718a0a
  • [9] B. A. Abiodun, A. A. Amosa, A. O. Olayode, A. T. Morufat, and S. A. Adedayo, "Traffic Light Control Using Adaptive Network Based Fuzzy Inference System," in Proceedings of 2014 International Conference on Artificial Intelligence & Manufacturing Engineering (ICAIME 2014), Dubai, 2014, pp. 156-161.
  • [10] C. I. Sitorus, "Perancangan Simulasi Pengatur Lampu Lalu Lintas Berdasarkan Volume Kendaraan Dan Lebar Jalan Berbasis Logika Fuzzy," Universitas Sumatera Utara. Medan, 2014.
  • [11] A. C. Soh, L. G. Rhung, and H. M. Sarkan, "MATLAB simulation of fuzzy traffic controller for multilane isolated intersection," International Journal on Computer Science and Engineering, vol. 2, no. 4, pp. 924-933, 2010.
  • [12] R. A. B. Utomo, D. A. Permana, and P. H. Rusmin, "Intelligent traffic light control system at two intersections using adaptive neuro-fuzzy inference system (ANFIS) method," in Earth and Space 2018: Engineering for Extreme Environments - Proceedings of the 16th Biennial International Conference on Engineering, Science, Construction, and Operations in Challenging Environments, 2018, pp. 976-990, doi: 10.1061/9780784481899.092. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087897263&doi=10.1061%2f9780784481899.092&partnerID=40&md5=4f5383efe750bc37b5c02894eed689de
  • [13] O. Awoyera, O. Sacko, O. Darboe, and O. Cynthia, "Anfis-Based Intelligent Traffic Control System (ITCS) for Developing Cities," Journal of Traffic and Logistics Engineering, pp. 18-22, 01/01 2019, doi: 10.18178/jtle.7.1.18-22.
  • [14] S. Araghi, A. Khosravi, and D. Creighton, "Design of an Optimal ANFIS Traffic Signal Controller by Using Cuckoo Search for an Isolated Intersection," in Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, 2016, pp. 2078-2083, doi: 10.1109/SMC.2015.363. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964506090&doi=10.1109%2fSMC.2015.363&partnerID=40&md5=c7a296a6f315a302fddc960737afb88a
  • [15] H. Zeynal, Z. Zakaria, A. Kor, and H. Torkamani, "An Improved ANFIS Based Traffic Flow Control through a Novel Approach on Input Selection," in ICPEA 2021 - 2021 IEEE International Conference in Power Engineering Application, 2021, pp. 161-166, doi: 10.1109/ICPEA51500.2021.9417843. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106411438&doi=10.1109%2fICPEA51500.2021.9417843&partnerID=40&md5=6840475e894b11d99817f9830a3ecc23
  • [16] U. Mittal and P. Chawla, "Traffic Green Signal Optimization using Adaptive Neuro-Fuzzy Inference System on Coordinated Intersections," in 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022, 2022, doi: 10.1109/ICRITO56286.2022.9964576. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144597052&doi=10.1109%2fICRITO56286.2022.9964576&partnerID=40&md5=f2bdc95617a984ec8965835cefb828ed
  • [17] M. E. M. Ali, A. Durdu, S. A. Çeltek, and A. Yilmaz, "An adaptive method for traffic signal control based on fuzzy logic with webster and modified webster formula using SUMO traffic simulator," IEEE Access, vol. 9, pp. 102985-102997, 2021.
  • [18] A. Zade and D. Dandekar, "Simulation of adaptive traffic signal controller in MATLAB simulink based on fuzzy inference system," in National Conference On Innovative Paradigms İn Engineering & Technology, 2012, vol. 9, p. 13.
  • [19] X. C. Vuong, R. F. Mou, T. T. Vu, and H. Van Nguyen, "An Adaptive Method for an Isolated Intersection under Mixed Traffic Conditions in Hanoi Based on ANFIS Using VISSIM-MATLAB," IEEE Access, Article vol. 9, pp. 166328-166338, 2021, doi: 10.1109/ACCESS.2021.3135418.
  • [20] G. Noureddine, "Intelligent Controllers for Permanent Magnet Synchronous Motor Drive Systems," AJIT, 01/01 2004.
Yıl 2024, Cilt: 13 Sayı: 1, 292 - 306, 24.03.2024
https://doi.org/10.17798/bitlisfen.1388486

Öz

Kaynakça

  • [1] B. Vatchova, Y. Boneva, and A. Gegov, "Modelling and Simulation of Traffic Light Control," Cybernetics and Information Technologies, vol. 23, no. 3, pp. 179-191, 2023, doi: 10.2478/cait-2023-0032.
  • [2] D. Jutury, N. Kumar, A. Sachan, Y. Daultani, and N. Dhakad, "Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport network," Applied Intelligence, vol. 53, no. 6, pp. 7132-7153, 2023, doi: 10.1007/s10489-022-03827-3.
  • [3] A. M. George, V. I. George, and M. A. George, "IOT based Smart Traffic Light Control System," in 2018 International Conference on Control, Power, Communication and Computing Technologies, ICCPCCT 2018, 2018, pp. 148-151, doi: 10.1109/ICCPCCT.2018.8574285. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060024823&doi=10.1109%2fICCPCCT.2018.8574285&partnerID=40&md5=c045a62856fde8f2d6192d92beb1c808
  • [4] S. B. Walukow, F. J. Doringin, R. E. Katuuk, and A. S. Wauran, "Regulation of the Real Time Traffic Light at Teling Intersection in Manado City by using Fuzzy Logic and ANFIS," in 2018 International Conference on Applied Science and Technology (iCAST), 2018: IEEE, pp. 259-262.
  • [5] K. M. Udofia, J. O. Emagbetere, and F. O. Edeko, "Dynamic traffic signal phase sequencing for an isolated intersection using ANFIS," Automation, Control and Intelligent Systems, vol. 2, no. 2, pp. 21-26, 2014.
  • [6] S. Araghi, A. Khosravi, and D. Creighton, "Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network," Expert Systems with Applications, vol. 42, no. 9, pp. 4422-4431, 2015, doi: 10.1016/j.eswa.2015.01.063.
  • [7] S. Araghi, A. Khosravi, and D. Creighton, "Comparing the performance of different types of distributed fuzzy-based traffic signal controllers," Journal of Intelligent and Fuzzy Systems, vol. 36, no. 6, pp. 6155-6166, 2019, doi: 10.3233/JIFS-181993.
  • [8] U. Andayani et al., "Simulation of Dynamic Traffic Light Setting Using Adaptive Neuro-Fuzzy Inference System (ANFIS)," in Journal of Physics: Conference Series, 2019, vol. 1235, 1 ed., doi: 10.1088/1742-6596/1235/1/012058. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070022009&doi=10.1088%2f1742- 6596%2f1235%2f1%2f012058&partnerID=40&md5=ab0dba18e53025008af8a2cfe6718a0a
  • [9] B. A. Abiodun, A. A. Amosa, A. O. Olayode, A. T. Morufat, and S. A. Adedayo, "Traffic Light Control Using Adaptive Network Based Fuzzy Inference System," in Proceedings of 2014 International Conference on Artificial Intelligence & Manufacturing Engineering (ICAIME 2014), Dubai, 2014, pp. 156-161.
  • [10] C. I. Sitorus, "Perancangan Simulasi Pengatur Lampu Lalu Lintas Berdasarkan Volume Kendaraan Dan Lebar Jalan Berbasis Logika Fuzzy," Universitas Sumatera Utara. Medan, 2014.
  • [11] A. C. Soh, L. G. Rhung, and H. M. Sarkan, "MATLAB simulation of fuzzy traffic controller for multilane isolated intersection," International Journal on Computer Science and Engineering, vol. 2, no. 4, pp. 924-933, 2010.
  • [12] R. A. B. Utomo, D. A. Permana, and P. H. Rusmin, "Intelligent traffic light control system at two intersections using adaptive neuro-fuzzy inference system (ANFIS) method," in Earth and Space 2018: Engineering for Extreme Environments - Proceedings of the 16th Biennial International Conference on Engineering, Science, Construction, and Operations in Challenging Environments, 2018, pp. 976-990, doi: 10.1061/9780784481899.092. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087897263&doi=10.1061%2f9780784481899.092&partnerID=40&md5=4f5383efe750bc37b5c02894eed689de
  • [13] O. Awoyera, O. Sacko, O. Darboe, and O. Cynthia, "Anfis-Based Intelligent Traffic Control System (ITCS) for Developing Cities," Journal of Traffic and Logistics Engineering, pp. 18-22, 01/01 2019, doi: 10.18178/jtle.7.1.18-22.
  • [14] S. Araghi, A. Khosravi, and D. Creighton, "Design of an Optimal ANFIS Traffic Signal Controller by Using Cuckoo Search for an Isolated Intersection," in Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, 2016, pp. 2078-2083, doi: 10.1109/SMC.2015.363. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964506090&doi=10.1109%2fSMC.2015.363&partnerID=40&md5=c7a296a6f315a302fddc960737afb88a
  • [15] H. Zeynal, Z. Zakaria, A. Kor, and H. Torkamani, "An Improved ANFIS Based Traffic Flow Control through a Novel Approach on Input Selection," in ICPEA 2021 - 2021 IEEE International Conference in Power Engineering Application, 2021, pp. 161-166, doi: 10.1109/ICPEA51500.2021.9417843. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106411438&doi=10.1109%2fICPEA51500.2021.9417843&partnerID=40&md5=6840475e894b11d99817f9830a3ecc23
  • [16] U. Mittal and P. Chawla, "Traffic Green Signal Optimization using Adaptive Neuro-Fuzzy Inference System on Coordinated Intersections," in 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022, 2022, doi: 10.1109/ICRITO56286.2022.9964576. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144597052&doi=10.1109%2fICRITO56286.2022.9964576&partnerID=40&md5=f2bdc95617a984ec8965835cefb828ed
  • [17] M. E. M. Ali, A. Durdu, S. A. Çeltek, and A. Yilmaz, "An adaptive method for traffic signal control based on fuzzy logic with webster and modified webster formula using SUMO traffic simulator," IEEE Access, vol. 9, pp. 102985-102997, 2021.
  • [18] A. Zade and D. Dandekar, "Simulation of adaptive traffic signal controller in MATLAB simulink based on fuzzy inference system," in National Conference On Innovative Paradigms İn Engineering & Technology, 2012, vol. 9, p. 13.
  • [19] X. C. Vuong, R. F. Mou, T. T. Vu, and H. Van Nguyen, "An Adaptive Method for an Isolated Intersection under Mixed Traffic Conditions in Hanoi Based on ANFIS Using VISSIM-MATLAB," IEEE Access, Article vol. 9, pp. 166328-166338, 2021, doi: 10.1109/ACCESS.2021.3135418.
  • [20] G. Noureddine, "Intelligent Controllers for Permanent Magnet Synchronous Motor Drive Systems," AJIT, 01/01 2004.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bulanık Hesaplama, Ulaşım ve Trafik, Endüstri Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Tuğçe İnağ 0000-0002-8800-6727

Murat Arıkan 0000-0003-1437-8939

Erken Görünüm Tarihi 21 Mart 2024
Yayımlanma Tarihi 24 Mart 2024
Gönderilme Tarihi 9 Kasım 2023
Kabul Tarihi 17 Ocak 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 1

Kaynak Göster

IEEE T. İnağ ve M. Arıkan, “A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 13, sy. 1, ss. 292–306, 2024, doi: 10.17798/bitlisfen.1388486.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

Bitlis Eren Üniversitesi Lisansüstü Eğitim Enstitüsü        
Beş Minare Mah. Ahmet Eren Bulvarı, Merkez Kampüs, 13000 BİTLİS        
E-posta: fbe@beu.edu.tr