Araştırma Makalesi
BibTex RIS Kaynak Göster

Effect of Yolo-Based Vehicle Detection and Manuel Vehicle Classification Methods on Simulation-Based Intersection Analysis

Yıl 2025, Cilt: 12 Sayı: 2, 665 - 690, 30.06.2025

Bahadır Ersoy Ulusoy , Doğukan Hazar , Yalcin Albayrak , Sevil Köfteci , Mehmet Arıkan Yalçın , Alican Demiral

https://doi.org/10.54287/gujsa.1647168

Öz

In complicated intersections, for measuring intersection performance using simulation programs (PTV Vissim, Aimsun, Sidra, etc.), decomposing camera recording data procured for vehicle counting according to vehicle type and movement direction is laborious. In this study, a software application was developed using YOLO (You Only Look Once) object detection model to classify vehicle types and estimation the number of vehicles from images obtained with camera recordings. The system aims to provide high-accuracy results in significantly less time compared to manual visual counting methods. The proposed model achieved a detection accuracy of 91.8%. The software was tested on video data from the Sampi Intersection, and the results were validated by comparing them to manual counting. Based on the obtained data, performance analyses were conducted using the PTV Vissim software by evaluating vehicle delays, determining service levels, and making comparative assessments. As a result of the simulation analysis, the vehicle delays, which are 38.82 seconds in the manual count method and 37.36 seconds in the software counting method, are between 35.1-55 seconds and correspond to the service level value D. The results demonstrate that YOLO-based vehicle counting and classification systems can offer an efficient and reliable alternative for traffic monitoring and intersection analysis.

Anahtar Kelimeler

Signalized Intersection Simulation Vehicle Count Vissim Artificial Intelligence

Destekleyen Kurum

Akdeniz University Scientific Research Projects Department

Proje Numarası

FYL-2018-3590

Kaynakça

  • Akbaş, A. (2001). Kent İçi Ulaşımında Ana Arterlerdeki Ulaşım Performansının Simülasyon Tabanlı Olarak Değerlendirilmesi. Ulaştırma Kongresi, 75–86.
  • Altun, İ., Dündar, S., & Yöntem, K. (2005). Yapay sinir ağları ile trafik akım kontrolü. Deprem Sempozyumu, 23–25.
  • An, H. K., Yue, W. L., & Kim, D. S. (2015). A Proposal of Two Signals Roundabout Analysis Method Using SIDRA6. KSCE Journal of Civil and Environmental Engineering Research, 35(5), 1111–1121. https://doi.org/10.12652/KSCE.2015.35.5.1111
  • Bao, X., Li, H., Xu, D., Jia, L., Ran, B., & Rong, J. (2016). Traffic Vehicle Counting in Jam Flow Conditions Using Low-Cost and Energy-Efficient Wireless Magnetic Sensors. Sensors, 16(11), 1868. https://doi.org/10.3390/S16111868
  • Bhosale, M. K., Patil, S. B., & Patil, B. B. (2023). Automatic Video Traffic Surveillance System with Number Plate Character Recognition Using Hybrid Optimization-Based YOLOv3 and Improved CNN. International Journal of Image and Graphics, 25(4), 2550041. https://doi.org/10.1142/S021946782550041X
  • Boxill, S. A., & Yu, L. (2000). An evaluation of traffic simulation models for supporting its. Houston, TX: Development Centre for Transportation Training and Research, Texas Southern University.
  • Charef, A., Jarir, Z., & Quafafou, M. (2024, May 2-4). Enhancing Road Safety: Automated Traffic Violation Detection and Counting System Using YOLO Algorithm. Proceedings of the 2024 Mediterranean Smart Cities Conference (MSCC), Morocco. https://doi.org/10.1109/MSCC62288.2024.10697076
  • Chung, J., & Sohn, K. (2018). Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1670–1675. https://doi.org/10.1109/TITS.2017.2732029
  • Cruz, F. R. G., Santos, C. J. R., & Vea, L. A. (2019, Novermber 29 - December 1). Classified Counting and Tracking of Local Vehicles in Manila Using Computer Vision. Proceedings of the 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Philippines. https://doi.org/10.1109/HNICEM48295.2019.9072808
  • Dai, Z., Song, H., Wang, X., Fang, Y., Yun, X., Zhang, Z., & Li, H. (2019). Video-based vehicle counting framework. IEEE Access, 7, 64460–64470. https://doi.org/10.1109/ACCESS.2019.2914254
  • Demiral, A. C., & Köfteci, S. (2019). Analysis of intersection performance with package program: Antalya Muratpaşa Sampi intersection example. International Journal of Environmental Science and Technology, 16(9), 5319–5324. https://doi.org/10.1007/s13762-019-02415-2
  • El-Khoreby, M. A., Abu-Bakar, S. A. R., Mokji, M. M., Omar, S. N., & Malik, N. U. R. (2019, September 17-19). Localized Background Subtraction Feature-Based Approach for Vehicle Counting. Proceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Malaysia, (pp. 324–328). https://doi.org/10.1109/ICSIPA45851.2019.8977795
  • Fellendorf, M., & Vortisch, P. (2001). Validation of the microscopic traffic flow model VISSIM in different real-world situations. Transportation Research Board 80th Annual Meeting, 11.
  • Findley, D. J., Cunningham, C. M., & Hummer, J. E. (2011). Comparison of mobile and manual data collection for roadway components. Transportation Research Part C: Emerging Technologies, 19(3), 521–540. https://doi.org/10.1016/J.TRC.2010.08.002
  • Ge, L., Dan, D., Koo, K. Y., & Chen, Y. (2023). An improved system for long-term monitoring of full-bridge traffic load distribution on long-span bridges. Structures, 54, 1076–1089. https://doi.org/10.1016/J.ISTRUC.2023.05.103
  • Güler, H. (2017). A new approach for road traffic accidents: Crash analysis segments model. Pamukkale University Journal of Engineering Sciences, 23(6), 707–717. https://doi.org/10.5505/pajes.2016.81542
  • Gupte, S., Masoud, O., Martin, R. F. K., & Papanikolopoulos, N. P. (2002). Detection and Classification of Vehicles. IEEE Transactions on Intelligent Transportation Systems, 3(1), 37–47. https://doi.org/10.1109/6979.994794
  • Hashmi, H. T., Ud-Din, S., Khan, M. A., Khan, J. A., Arshad, M., & Hassan, M. U. (2024). Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies. Infrastructures, 9(5), 87. https://doi.org/10.3390/INFRASTRUCTURES9050087
  • Indolia, S., Goswami, A. K., Mishra, S. P., & Asopa, P. (2018). Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach. Procedia Computer Science, 132, 679–688. https://doi.org/10.1016/J.PROCS.2018.05.069
  • Kolluri, J., & Das, R. (2023). Intelligent multimodal pedestrian detection using hybrid metaheuristic optimization with deep learning model. Image and Vision Computing, 131, 104628. https://doi.org/10.1016/J.IMAVIS.2023.104628
  • Kulkarni, M. M., Chaudhari, A. A., Srinivasan, K. K., Chilukuri, B. R., Treiber, M., & Okhrin, O. (2025). Leader–follower identification with vehicle-following calibration for non-lane-based traffic. Transportation Research Part C: Emerging Technologies, 171, 104940. https://doi.org/10.1016/J.TRC.2024.104940
  • Li, S., Chang, F., Liu, C., & Li, N. (2020). Vehicle counting and traffic flow parameter estimation for dense traffic scenes. IET Intelligent Transport Systems, 14(12), 1517–1523. https://doi.org/10.1049/IET-ITS.2019.0521
  • Lin, J. P., & Sun, M. Te. (2018, Novermber 30 – December 2). A YOLO-Based Traffic Counting System. Proceedings of the 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI), Taiwan. (pp. 82–85). https://doi.org/10.1109/TAAI.2018.00027
  • Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common Objects in Context. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8693 LNCS(PART 5), 740–755. https://doi.org/10.1007/978-3-319-10602-1_48
  • Ma, X., Wei, W., Dong, J., Zheng, B., & Ma, J. (2023, June 18-23). RTOD-YOLO: Traffic Object Detection in UAV Images Based on Visual Attention and Re-parameterization. Proceedings of the 2023 International Joint Conference on Neural Networks (IJCNN), Australia. https://doi.org/10.1109/IJCNN54540.2023.10191514
  • MassDot (2012, January 5). A Guide on Traffic Analysis Tools Revised. https://nanopdf.com/download/a-guide-on-traffic-analysis-tools-revised-october-5-2012_pdf
  • Mesci, Y. (2019, April 29). YOLO Algoritmasını Anlamak. Son yıllarda nesne tespiti alanında… | by Yiğit Mesci | Deep Learning Türkiye | Medium. (Accessed: 11/04/2025) https://medium.com/deep-learning-turkiye/yolo-algoritmas%C4%B1n%C4%B1-anlamak-290f2152808f
  • Pazar, Ş., Bulut, M., Uysal, C., (2020). Yapay Zeka Tabanlı Araç Algılama Sistemi Geliştirilmesi, Bilim, Teknoloji ve Mühendislik Araştırmaları Dergisi, 1(1), 31-37.
  • Oltean, G., Florea, C., Orghidan, R., & Oltean, V. (2019, October 23-26). Towards Real Time Vehicle Counting using YOLO-Tiny and Fast Motion Estimation. Proceedings of the 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME), Romania, (pp. 240–243). https://doi.org/10.1109/SIITME47687.2019.8990708
  • Paľo, J., Caban, J., Kiktová, M., & Černický. (2019). The comparison of automatic traffic counting and manual traffic counting. IOP Conference Series: Materials Science and Engineering, 710(1), 012041. https://doi.org/10.1088/1757-899X/710/1/012041
  • Papageorgiou, G. N. (2006). Towards a Mıcroscopıc Sımulatıon Model for Traffıc Management: A Computer-Based Approach. IFAC Proceedings Volumes, 39(12), 403–411. https://doi.org/10.3182/20060829-3-NL-2908.00070
  • Pişkin, M. (2020, May 27). OpenCV Nedir? Bileşenleri ve Alternatifleri | Mesut Pişkin | Blog. https://mesutpiskin.com/blog/opencv-nedir.html
  • Sengoz, N. (2017, Mart 4). Yapay Sinir Ağları - Derin Öğrenme | Deep Learning. https://www.derinogrenme.com/2017/03/04/yapay-sinir-aglari/
  • Sivaraman, S., & Trivedi, M. M. (2013). Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1773–1795. https://doi.org/10.1109/TITS.2013.2266661
  • Sooksatra, S., Yoshitaka, A., Kondo, T., & Bunnun, P. (2019, Novermber 26-29). The density-aware estimation network for vehicle counting in traffic surveillance system. Proceedings of the- 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Italy, (pp. 231–238). https://doi.org/10.1109/SITIS.2019.00047
  • Toth, C., Suh, W., Elango, V., Sadana, R., Guin, A., Hunter, M., & Guensler, R. (2013). Tablet-Based Traffic Counting Application Designed to Minimize Human Error. Transportation Research Record, 2339, 39–46. https://doi.org/10.3141/2339-05
  • Wikipedia (2020). Python. https://tr.wikipedia.org/wiki/Python
  • Yao, L. (2019, December 9-11). An Effective Vehicle Counting Approach Based on CNN. Proceedings of the 2019 IEEE 2nd International Conference on Electronics and Communication Engineering (ICECE), China, (pp. 15–19). https://doi.org/10.1109/ICECE48499.2019.9058582
  • Yıldırım, Z. B., Özdemir, B. E., & Eren, E. (2019). Trafikteki Araç Sayımlarının Farklı Görüntü İşleme Teknikleri Kullanılarak Karşılaştırılması. Proceedings of the 2nd International Congress on Engineering and Agriculture, (pp. 242–248).
  • Zheng, P., & Mike, M. (2012). An Investigation on the Manual Traffic Count Accuracy. Procedia - Social and Behavioral Sciences, 43, 226–231. https://doi.org/10.1016/J.SBSPRO.2012.04.095

Yıl 2025, Cilt: 12 Sayı: 2, 665 - 690, 30.06.2025

Bahadır Ersoy Ulusoy , Doğukan Hazar , Yalcin Albayrak , Sevil Köfteci , Mehmet Arıkan Yalçın , Alican Demiral

https://doi.org/10.54287/gujsa.1647168

Öz

Proje Numarası

FYL-2018-3590

Kaynakça

  • Akbaş, A. (2001). Kent İçi Ulaşımında Ana Arterlerdeki Ulaşım Performansının Simülasyon Tabanlı Olarak Değerlendirilmesi. Ulaştırma Kongresi, 75–86.
  • Altun, İ., Dündar, S., & Yöntem, K. (2005). Yapay sinir ağları ile trafik akım kontrolü. Deprem Sempozyumu, 23–25.
  • An, H. K., Yue, W. L., & Kim, D. S. (2015). A Proposal of Two Signals Roundabout Analysis Method Using SIDRA6. KSCE Journal of Civil and Environmental Engineering Research, 35(5), 1111–1121. https://doi.org/10.12652/KSCE.2015.35.5.1111
  • Bao, X., Li, H., Xu, D., Jia, L., Ran, B., & Rong, J. (2016). Traffic Vehicle Counting in Jam Flow Conditions Using Low-Cost and Energy-Efficient Wireless Magnetic Sensors. Sensors, 16(11), 1868. https://doi.org/10.3390/S16111868
  • Bhosale, M. K., Patil, S. B., & Patil, B. B. (2023). Automatic Video Traffic Surveillance System with Number Plate Character Recognition Using Hybrid Optimization-Based YOLOv3 and Improved CNN. International Journal of Image and Graphics, 25(4), 2550041. https://doi.org/10.1142/S021946782550041X
  • Boxill, S. A., & Yu, L. (2000). An evaluation of traffic simulation models for supporting its. Houston, TX: Development Centre for Transportation Training and Research, Texas Southern University.
  • Charef, A., Jarir, Z., & Quafafou, M. (2024, May 2-4). Enhancing Road Safety: Automated Traffic Violation Detection and Counting System Using YOLO Algorithm. Proceedings of the 2024 Mediterranean Smart Cities Conference (MSCC), Morocco. https://doi.org/10.1109/MSCC62288.2024.10697076
  • Chung, J., & Sohn, K. (2018). Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1670–1675. https://doi.org/10.1109/TITS.2017.2732029
  • Cruz, F. R. G., Santos, C. J. R., & Vea, L. A. (2019, Novermber 29 - December 1). Classified Counting and Tracking of Local Vehicles in Manila Using Computer Vision. Proceedings of the 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Philippines. https://doi.org/10.1109/HNICEM48295.2019.9072808
  • Dai, Z., Song, H., Wang, X., Fang, Y., Yun, X., Zhang, Z., & Li, H. (2019). Video-based vehicle counting framework. IEEE Access, 7, 64460–64470. https://doi.org/10.1109/ACCESS.2019.2914254
  • Demiral, A. C., & Köfteci, S. (2019). Analysis of intersection performance with package program: Antalya Muratpaşa Sampi intersection example. International Journal of Environmental Science and Technology, 16(9), 5319–5324. https://doi.org/10.1007/s13762-019-02415-2
  • El-Khoreby, M. A., Abu-Bakar, S. A. R., Mokji, M. M., Omar, S. N., & Malik, N. U. R. (2019, September 17-19). Localized Background Subtraction Feature-Based Approach for Vehicle Counting. Proceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Malaysia, (pp. 324–328). https://doi.org/10.1109/ICSIPA45851.2019.8977795
  • Fellendorf, M., & Vortisch, P. (2001). Validation of the microscopic traffic flow model VISSIM in different real-world situations. Transportation Research Board 80th Annual Meeting, 11.
  • Findley, D. J., Cunningham, C. M., & Hummer, J. E. (2011). Comparison of mobile and manual data collection for roadway components. Transportation Research Part C: Emerging Technologies, 19(3), 521–540. https://doi.org/10.1016/J.TRC.2010.08.002
  • Ge, L., Dan, D., Koo, K. Y., & Chen, Y. (2023). An improved system for long-term monitoring of full-bridge traffic load distribution on long-span bridges. Structures, 54, 1076–1089. https://doi.org/10.1016/J.ISTRUC.2023.05.103
  • Güler, H. (2017). A new approach for road traffic accidents: Crash analysis segments model. Pamukkale University Journal of Engineering Sciences, 23(6), 707–717. https://doi.org/10.5505/pajes.2016.81542
  • Gupte, S., Masoud, O., Martin, R. F. K., & Papanikolopoulos, N. P. (2002). Detection and Classification of Vehicles. IEEE Transactions on Intelligent Transportation Systems, 3(1), 37–47. https://doi.org/10.1109/6979.994794
  • Hashmi, H. T., Ud-Din, S., Khan, M. A., Khan, J. A., Arshad, M., & Hassan, M. U. (2024). Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies. Infrastructures, 9(5), 87. https://doi.org/10.3390/INFRASTRUCTURES9050087
  • Indolia, S., Goswami, A. K., Mishra, S. P., & Asopa, P. (2018). Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach. Procedia Computer Science, 132, 679–688. https://doi.org/10.1016/J.PROCS.2018.05.069
  • Kolluri, J., & Das, R. (2023). Intelligent multimodal pedestrian detection using hybrid metaheuristic optimization with deep learning model. Image and Vision Computing, 131, 104628. https://doi.org/10.1016/J.IMAVIS.2023.104628
  • Kulkarni, M. M., Chaudhari, A. A., Srinivasan, K. K., Chilukuri, B. R., Treiber, M., & Okhrin, O. (2025). Leader–follower identification with vehicle-following calibration for non-lane-based traffic. Transportation Research Part C: Emerging Technologies, 171, 104940. https://doi.org/10.1016/J.TRC.2024.104940
  • Li, S., Chang, F., Liu, C., & Li, N. (2020). Vehicle counting and traffic flow parameter estimation for dense traffic scenes. IET Intelligent Transport Systems, 14(12), 1517–1523. https://doi.org/10.1049/IET-ITS.2019.0521
  • Lin, J. P., & Sun, M. Te. (2018, Novermber 30 – December 2). A YOLO-Based Traffic Counting System. Proceedings of the 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI), Taiwan. (pp. 82–85). https://doi.org/10.1109/TAAI.2018.00027
  • Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common Objects in Context. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8693 LNCS(PART 5), 740–755. https://doi.org/10.1007/978-3-319-10602-1_48
  • Ma, X., Wei, W., Dong, J., Zheng, B., & Ma, J. (2023, June 18-23). RTOD-YOLO: Traffic Object Detection in UAV Images Based on Visual Attention and Re-parameterization. Proceedings of the 2023 International Joint Conference on Neural Networks (IJCNN), Australia. https://doi.org/10.1109/IJCNN54540.2023.10191514
  • MassDot (2012, January 5). A Guide on Traffic Analysis Tools Revised. https://nanopdf.com/download/a-guide-on-traffic-analysis-tools-revised-october-5-2012_pdf
  • Mesci, Y. (2019, April 29). YOLO Algoritmasını Anlamak. Son yıllarda nesne tespiti alanında… | by Yiğit Mesci | Deep Learning Türkiye | Medium. (Accessed: 11/04/2025) https://medium.com/deep-learning-turkiye/yolo-algoritmas%C4%B1n%C4%B1-anlamak-290f2152808f
  • Pazar, Ş., Bulut, M., Uysal, C., (2020). Yapay Zeka Tabanlı Araç Algılama Sistemi Geliştirilmesi, Bilim, Teknoloji ve Mühendislik Araştırmaları Dergisi, 1(1), 31-37.
  • Oltean, G., Florea, C., Orghidan, R., & Oltean, V. (2019, October 23-26). Towards Real Time Vehicle Counting using YOLO-Tiny and Fast Motion Estimation. Proceedings of the 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME), Romania, (pp. 240–243). https://doi.org/10.1109/SIITME47687.2019.8990708
  • Paľo, J., Caban, J., Kiktová, M., & Černický. (2019). The comparison of automatic traffic counting and manual traffic counting. IOP Conference Series: Materials Science and Engineering, 710(1), 012041. https://doi.org/10.1088/1757-899X/710/1/012041
  • Papageorgiou, G. N. (2006). Towards a Mıcroscopıc Sımulatıon Model for Traffıc Management: A Computer-Based Approach. IFAC Proceedings Volumes, 39(12), 403–411. https://doi.org/10.3182/20060829-3-NL-2908.00070
  • Pişkin, M. (2020, May 27). OpenCV Nedir? Bileşenleri ve Alternatifleri | Mesut Pişkin | Blog. https://mesutpiskin.com/blog/opencv-nedir.html
  • Sengoz, N. (2017, Mart 4). Yapay Sinir Ağları - Derin Öğrenme | Deep Learning. https://www.derinogrenme.com/2017/03/04/yapay-sinir-aglari/
  • Sivaraman, S., & Trivedi, M. M. (2013). Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1773–1795. https://doi.org/10.1109/TITS.2013.2266661
  • Sooksatra, S., Yoshitaka, A., Kondo, T., & Bunnun, P. (2019, Novermber 26-29). The density-aware estimation network for vehicle counting in traffic surveillance system. Proceedings of the- 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Italy, (pp. 231–238). https://doi.org/10.1109/SITIS.2019.00047
  • Toth, C., Suh, W., Elango, V., Sadana, R., Guin, A., Hunter, M., & Guensler, R. (2013). Tablet-Based Traffic Counting Application Designed to Minimize Human Error. Transportation Research Record, 2339, 39–46. https://doi.org/10.3141/2339-05
  • Wikipedia (2020). Python. https://tr.wikipedia.org/wiki/Python
  • Yao, L. (2019, December 9-11). An Effective Vehicle Counting Approach Based on CNN. Proceedings of the 2019 IEEE 2nd International Conference on Electronics and Communication Engineering (ICECE), China, (pp. 15–19). https://doi.org/10.1109/ICECE48499.2019.9058582
  • Yıldırım, Z. B., Özdemir, B. E., & Eren, E. (2019). Trafikteki Araç Sayımlarının Farklı Görüntü İşleme Teknikleri Kullanılarak Karşılaştırılması. Proceedings of the 2nd International Congress on Engineering and Agriculture, (pp. 242–248).
  • Zheng, P., & Mike, M. (2012). An Investigation on the Manual Traffic Count Accuracy. Procedia - Social and Behavioral Sciences, 43, 226–231. https://doi.org/10.1016/J.SBSPRO.2012.04.095
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ulaştırma Mühendisliği
Bölüm İnşaat Mühendisliği
Yazarlar

Bahadır Ersoy Ulusoy 0000-0003-3367-8312

Doğukan Hazar 0009-0009-2285-2515

Yalcin Albayrak 0000-0002-5632-0869

Sevil Köfteci 0000-0002-5096-2545

Mehmet Arıkan Yalçın 0000-0002-8916-1411

Alican Demiral 0009-0001-8241-8253

Proje Numarası FYL-2018-3590
Erken Görünüm Tarihi 30 Haziran 2025
Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 27 Şubat 2025
Kabul Tarihi 10 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 12 Sayı: 2

Kaynak Göster

APA Ulusoy, B. E., Hazar, D., Albayrak, Y., Köfteci, S., vd. (2025). Effect of Yolo-Based Vehicle Detection and Manuel Vehicle Classification Methods on Simulation-Based Intersection Analysis. Gazi University Journal of Science Part A: Engineering and Innovation, 12(2), 665-690. https://doi.org/10.54287/gujsa.1647168
 Kapak Resmi İndir