YOLO-Based Real-Time Artificial Intelligence Traffic Counting for Urban Transportation Monitoring in Surakarta: Implications for SDG 11
DOI:
https://doi.org/10.63230/jocsis.2.1.152Keywords:
Artificial Intelligence, Traffic Counting, Urban Transportation, Vehicle Detection, YOLOAbstract
Objective: To develop and evaluate an artificial intelligence (AI)-based traffic counting system using the YOLO (You Only Look Once) deep learning algorithm to provide accurate and real-time traffic volume data for urban transportation management. Method: Employing a deep learning approach by implementing the YOLO algorithm for vehicle detection and traffic counting. Traffic video data from road objects in Surakarta City were processed to identify and classify various vehicle types. The AI-generated traffic counting results were then compared with manual traffic survey data to assess the system’s accuracy and effectiveness. Results: The findings indicate that the proposed AI-based traffic counting system can accurately detect and classify multiple vehicle categories, including cars, motorcycles, trucks, buses, bicycles, and Bajaj. The traffic-counting data produced by the system were highly readable and reliable. Comparison with manual traffic surveys showed that the AI-generated results were very similar while requiring significantly less time and human resources. The system achieved nearly 100% consistency with the available secondary traffic volume data, demonstrating its effectiveness in monitoring urban traffic conditions. Novelty: Application of the YOLO deep learning algorithm for automated traffic counting in the urban road environment of Surakarta City. The proposed system provides a practical and efficient alternative to conventional manual traffic surveys by delivering accurate, real-time traffic data with minimal human intervention, thereby supporting more effective urban transportation planning and management. These contributions are also relevant to SDG 11 (Sustainable Cities and Communities) by enabling data-driven traffic monitoring and facilitating smarter, more sustainable urban mobility management.
References
Adarsh, P., Rathi, P., & Kumar, M. (2020, March). YOLO v3-Tiny: Object detection and recognition using one stage improved model. In 2020 6th international conference on advanced computing and communication systems (ICACCS) (pp. 687-694). IEEE. https://doi.org/10.1109/ICACCS48705.2020.9074315
Carvalho Barbosa, R., de Oliveira, A. S., de Almeida, A. F., & colleagues. (2020). Lightweight PVIDNet: A priority vehicle detection network model for intelligent traffic systems. Sensors, 20(21), 6218. https://doi.org/10.3390/s20216218
Chatterjee, K., & McDonald, M. (2004). Effectiveness of using variable message signs to disseminate dynamic traffic information: Evidence from field trials in European cities. Transport Reviews, 24(5), 559–585. https://doi.org/10.1080/0144164042000196080
Chen, P.-Y., Chang, M.-C., Hsieh, J.-W., & Chen, Y.-S. (2021). Paper on feature pyramid network for object detection. IEEE Transactions on Image Processing, 30, 9099–9111. https://doi.org/10.1109/TIP.2021.3118953
Chen, Y., Wang, W., & Chen, X. M. (2023). Bibliometric methods in traffic flow prediction based on artificial intelligence. Expert Systems with Applications, 228, 120421. https://doi.org/10.1016/j.eswa.2023.120421
Choksi, M., Zaveri, M. A., & Anand, S. (2018, September). Traffic surveillance for smart city in internet of things environment. In Proceedings of SAI Intelligent Systems Conference (pp. 189-204). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01057-7_16
Gai, Y., He, W., & Zhou, Z. (2021, November). Pedestrian target tracking based on DeepSORT with YOLOv5. In 2021 2nd International Conference on Computer Engineering and Intelligent Control (ICCEIC) (pp. 1-5). IEEE. https://doi.org/10.1109/ICCEIC54227.2021.00008
Gan, H. M., Fernando, S., & Molina-Solana, M. (2021). Deep learning for intelligent traffic monitoring and management. Expert Systems with Applications, 182, 115154. https://doi.org/10.1016/j.eswa.2021.115154
Hendratmoko, P., & Dewantoro, Y. E. R. U. (2018). Pemetaan emisi CO₂ hasil kontribusi kegiatan transportasi di Kota Tegal Jawa Tengah. Jurnal Keselamatan Transportasi Jalan, 5(2), 19–28. https://doi.org/10.46447/ktj.v5i2.46
Jiang, H., Wang, Y., Zhang, X., Li, J., Chen, Y., & Liu, H. (2020). Effects of combined radio frequency with hot water blanching on enzyme inactivation, color and texture of sweet potato. Innovative Food Science & Emerging Technologies, 66, 102513. https://doi.org/10.1016/j.ifset.2020.102513
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019). A survey of deep learning-based object detection. IEEE Access, 7, 128837–128868. https://doi.org/10.1109/ACCESS.2019.2939201
Kamkar, S., & Safabakhsh, R. (2016). Vehicle detection, counting, and classification in various conditions. IET Intelligent Transport Systems, 10(6), 406–413. https://doi.org/10.1049/iet-its.2015.0157
Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: Techniques, datasets and challenges. Cybersecurity, 2(1), 20. https://doi.org/10.1186/s42400-019-0038-7
Leroux, S., Li, B., & Simoens, P. (2022). Automated training of location-specific edge models for traffic counting. Computers & Electrical Engineering, 99, 107763. https://doi.org/10.1016/j.compeleceng.2022.107763
Liu, J.-E., & An, F.-P. (2020). Intelligent traffic monitoring system based on computer vision and deep learning. Scientific Programming, 2020, 7607612. https://doi.org/10.1155/2020/7607612
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep learning for generic object detection: A survey. International Journal of Computer Vision, 128, 261–318. https://doi.org/10.1007/s11263-019-01247-4
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In ECCV 2016 (pp. 21–37). Springer. https://doi.org/10.1007/978-3-319-46448-0_2
Müller, J., & Dietmayer, K. (2018, November). Detecting traffic lights by single shot detection. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 266-273). IEEE. https://doi.org/10.1109/ITSC.2018.8569683
Musa, A. A., Ibrahim, M., Bello, S., & Abdullahi, H. (2023). Sustainable traffic monitoring using AI-based systems. Sustainability, 15(13), 9859. https://doi.org/10.3390/su15139859
Naveed, Q. N., Alqahtani, H., Khan, R. U., Alshehri, M., & Abdul Rasheed, M. A. (2022). Intelligent traffic surveillance system using sensor networks. Sensors, 22(9), 3333. https://doi.org/10.3390/s22093333
Ngeni, F., Mwakalonge, J., & Siuhi, S. (2024). Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing. Journal of Traffic and Transportation Engineering, 11(1). https://doi.org/10.1016/j.jtte.2023.05.006
Oltean, G., Florea, C., Orghidan, R., & Oltean, V. (2019, October). Towards real time vehicle counting using yolo-tiny and fast motion estimation. In 2019 IEEE 25th international symposium for design and technology in electronic packaging (SIITME) (pp. 240-243). IEEE. https://doi.org/10.1109/SIITME47687.2019.8990708
Oreski, G. (2023). YOLO*C—Adding context improves YOLO performance. Neurocomputing, 555, 126655. https://doi.org/10.1016/j.neucom.2023.126655
Patel, D., Hosseini, P., & Jalayer, M. (2023). A framework for proactive safety evaluation of intersections using surrogate safety measures and non-compliance behavior. Accident Analysis & Prevention, 192, 107264. https://doi.org/10.1016/j.aap.2023.107264
Pathak, A. R., Pandey, M., & Rautaray, S. (2018). Application of deep learning for object detection. Procedia Computer Science, 132, 1706–1717. https://doi.org/10.1016/j.procs.2018.05.144
Pratama, Y., Rasywir, E., Sunoto, A., & Irawan, I. (2021). Application of YOLO (You Only Look Once) V4 with preprocessing image and network experiment. International Journal of Informatics and Computer Science, 5(3), 280–286. https://doi.org/10.30865/ijics.v5i3.3386
Redmon, J. (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767. https://doi.org/10.48550/arXiv.1804.02767
Soleimanitaleb, Z., & Keyvanrad, M. A. (2019). Object tracking methods: A review. In 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 282–288. https://doi.org/10.1109/ICCKE.2019.8722833
Srivastava, J. R., & Sudarshan, T. S. B. (2013, May). Intelligent traffic management with wireless sensor networks. In 2013 ACS International Conference on Computer Systems and Applications (AICCSA) (pp. 1-4). IEEE. https://doi.org/10.1109/AICCSA.2013.6616429
Unzilatirrizqi, Y., Istiyanto, B., & Maulana, A. (2019). The CO₂ emissions distribution due to contribution of transportation activities in Tegal City, Central Java. In Proceedings of the 11th Asia Pacific Transportation and Environment Conference (APTE 2018) (pp. 187–190). Atlantis Press. https://doi.org/10.2991/apte-18.2019.25
Wen, Y., Pan, J. L., & Le, J. F. (2007). Survey on application of wireless sensor networks for traffic monitoring. In International Conference on Transportation Engineering 2007 (pp. 2079-2084). https://doi.org/10.1061/40932(246)340
Zhang, X., & Zhu, X. (2019, July). Vehicle detection in the aerial infrared images via an improved Yolov3 network. In 2019 IEEE 4th international conference on signal and image processing (ICSIP) (pp. 372-376). IEEE. https://doi.org/10.1109/SIPROCESS.2019.8868430
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
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