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Intelligent Traffic Management System Using Mask Regions-Convolutional Neural Network
Corresponding Author(s) : Muhammad Kemal Pasha
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 10, No. 3, August 2025
Abstract
Urban centers worldwide continue to face challenges in traffic management due to outdated traffic signal infrastructure. This study aims to develop an intelligent traffic management system by implementing the Mask Regions-Convolutional Neural Network (MR-CNN) algorithm for real-time vehicle detection and traffic flow optimization. Utilizing the CRISP-DM framework, this research processes CCTV footage from the Pasteur-Pasopati intersection in Bandung to identify and quantify vehicles dynamically. The proposed system leverages an enhanced Mask R-CNN model with a ResNet-50 FPN backbone to improve detection accuracy. Experimental results demonstrate an 80% vehicle detection accuracy, with a macro-average precision of 0.89, recall of 0.83, and an F1-score of 0.82. These findings highlight the system’s capability to replace conventional fixed-time traffic signals with a more adaptive approach, adjusting green light durations based on real-time traffic density. The proposed solution has significant practical implications for reducing congestion and improving traffic flow efficiency in urban environments.
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- B. Pishue, “2021 INRIX Global Traffic Scorecard,” p. 21, 2021.
- B. Pishue, “2021 INRIX Global Traffic Scorecard,” p. 23, 2021.
- Badan Pusat Statistik, “Jumlah Kendaraan Indonesia 2022.” Accessed: Jun. 12, 2024.
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- H. Tran Ngoc, K. Hoang Nguyen, H. Khanh Hua, H. Vu Nhu Nguyen, and L.-D. Quach, “Optimizing YOLO Performance for Traffic Light Detection and End-to-End Steering Control for Autonomous Vehicles in Gazebo-ROS2.” https://dx.doi.org/10.14569/IJACSA.2023.0140752
- H. Lai, L. Chen, W. Liu, Z. Yan, and S. Ye, “STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments,” Sensors, vol. 23, no. 11, Jun. 2023. https://doi.org/10.3390/s23115307
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- C. Schröer, F. Kruse, and J. M. Gómez, “A Systematic Literature Review on Applying CRISP-DM Process Model,” Procedia Comput Sci, vol. 181, pp. 526–534, 2021. https://doi.org/10.1016/j.procs.2021.01.199
- S. Jaggia, A. Kelly, K. Lertwachara, and L. Chen, “Applying the CRISP‐DM Framework for Teaching Business Analytics,” Decision Sciences Journal of Innovative Education, vol. 18, no. 4, pp. 612–634, Oct. 2020. https://doi.org/10.1111/dsji.12222
- K. Abedi, J. Codjoe, R. Thapa, and V. Gopu, “Making Data-Driven Transportation Decisions for Freight Operations,” J Transp Technol, vol. 13, no. 03, pp. 411–442, 2023. https://doi.org/10.4236/jtts.2023.133020
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References
B. Pishue, “2021 INRIX Global Traffic Scorecard,” p. 21, 2021.
B. Pishue, “2021 INRIX Global Traffic Scorecard,” p. 23, 2021.
Badan Pusat Statistik, “Jumlah Kendaraan Indonesia 2022.” Accessed: Jun. 12, 2024.
K. Lubis, “Analysis of the Characteristics of Public Transportation Modes to Users of Land Transportation Modes as City Transportation Within the Province,” Online, 2019. https://doi.org/10.30743/but.v15i1.1877
J. Dwijoko Ansusanto and S. Tanggu, “Analisis Kinerja Dan Manajemen Pada Simpang Dengan Derajat Kejenuhan Tinggi Performance Analysis and Management on Saturated Traffic Intersection.”
J. Li, Y. Zhang, and Y. Chen, “A Self-Adaptive Traffic Light Control System Based on Speed of Vehicles,” in 2016 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), IEEE, Aug. 2016, pp. 382–388. https://doi.org/10.1109/QRS-C.2016.58
A. Muralidharan, R. Pedarsani, and P. Varaiya, “Analysis of fixed-time control,” Transportation Research Part B: Methodological, vol. 73, pp. 81–90, Mar. 2015. https://doi.org/10.1016/j.trb.2014.12.002
S. Zhao, G. Qi, P. Li, and W. Guan, “The aggressive driving performance caused by congestion based on behavior and EEG analysis,” J Safety Res, vol. 91, pp. 381–392, Dec. 2024. https://doi.org/10.1016/j.jsr.2024.10.004
Z. Fahrunnisa and R. Arief Setyawan, “Adaptive Traffic Light Signal Control Using Fuzzy Logic Based on Real-Time Vehicle Detection from Video Surveillance,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 10, no. 2, pp. 235–251, 2024. https://doi.org/10.26555/jiteki.v10i2.28712
F. Zahwa, C.-T. Cheng, and M. Simic, “Novel Intelligent Traffic Light Controller Design,” Machines, vol. 12, no. 7, p. 469, Jul. 2024. https://doi.org/10.3390/machines12070469
A. N. Aulia Yusuf, A. Setyo Arifin, and F. Yuli Zulkifli, “Recent development of smart traffic lights,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 10, no. 1, p. 224, Mar. 2021. https://doi.org/10.11591/ijai.v10.i1.pp224-233
et al Abigail See, “Mask-RCNN for object detection and instance segmentation on Keras and TensorFlow.” Accessed: Jun. 19, 2024.
S. Sumahasan, “Object Detection using Deep Learning Algorithm CNN,” Int J Res Appl Sci Eng Technol, vol. 8, no. 7, pp. 1578–1584, Jul. 2020. https://doi.org/10.22214/ijraset.2020.30594
M. Li, H. Zhu, H. Chen, L. Xue, and T. Gao, “Research on Object Detection Algorithm Based on Deep Learning,” J Phys Conf Ser, vol. 1995, no. 1, p. 012046, Aug. 2021. https://doi.org/10.1088/1742-6596/1995/1/012046
F. Charli, H. Syaputra, M. Akbar3, S. Sauda, and F. Panjaitan, “Implementasi Metode Faster Region Convolutional Neural Network (Faster R-CNN) Untuk Pengenalan Jenis Burung Lovebird,” 2020. https://doi.org/10.51519/journalita.volume1.isssue3.year2020.page185-197
L. Du, R. Zhang, and X. Wang, “Overview of two-stage object detection algorithms,” J Phys Conf Ser, vol. 1544, no. 1, p. 012033, May 2020. https://doi.org/10.1088/1742-6596/1544/1/012033
Z. Gongguo and W. Junhao, “An improved small target detection method based on Yolo V3,” in Proceedings - 2021 International Conference on Electronics, Circuits and Information Engineering, ECIE 2021, Institute of Electrical and Electronics Engineers Inc., Jan. 2021, pp. 220–223. https://doi.org/10.1109/ECIE52353.2021.00054
G. ÇINARER, “Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm,” Düzce Üniversitesi Bilim ve Teknoloji Dergisi, vol. 12, no. 1, pp. 219–229, Jan. 2024. https://doi.org/10.29130/dubited.1214901
H. Tran Ngoc, K. Hoang Nguyen, H. Khanh Hua, H. Vu Nhu Nguyen, and L.-D. Quach, “Optimizing YOLO Performance for Traffic Light Detection and End-to-End Steering Control for Autonomous Vehicles in Gazebo-ROS2.” https://dx.doi.org/10.14569/IJACSA.2023.0140752
H. Lai, L. Chen, W. Liu, Z. Yan, and S. Ye, “STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments,” Sensors, vol. 23, no. 11, Jun. 2023. https://doi.org/10.3390/s23115307
M. Hidayat, Z. Bilfaqih, Y. A. Hady, and M. A. Tampubolon, “Smart Traffic Light Using YOLO Based Camera with Deep Reinforcement Learning Algorithm,” 2023. https://doi.org/10.12962/jaree.v7i1.335
A. Vidali, L. Crociani, G. Vizzari, and S. Bandini, “A Deep Reinforcement Learning Approach to Adaptive Traffic Lights Management,” 2019.
C. Schröer, F. Kruse, and J. M. Gómez, “A Systematic Literature Review on Applying CRISP-DM Process Model,” Procedia Comput Sci, vol. 181, pp. 526–534, 2021. https://doi.org/10.1016/j.procs.2021.01.199
S. Jaggia, A. Kelly, K. Lertwachara, and L. Chen, “Applying the CRISP‐DM Framework for Teaching Business Analytics,” Decision Sciences Journal of Innovative Education, vol. 18, no. 4, pp. 612–634, Oct. 2020. https://doi.org/10.1111/dsji.12222
K. Abedi, J. Codjoe, R. Thapa, and V. Gopu, “Making Data-Driven Transportation Decisions for Freight Operations,” J Transp Technol, vol. 13, no. 03, pp. 411–442, 2023. https://doi.org/10.4236/jtts.2023.133020
S. Zia, B. Yuksel, D. Yuret, and Y. Yemez, “RGB-D Object Recognition Using Deep Convolutional Neural Networks,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), IEEE, Oct. 2017, pp. 887–894. https://doi.org/10.1109/ICCVW.2017.109
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” Apr. 2020. https://doi.org/10.48550/arXiv.2004.10934
M. A. Khasawneh and A. Awasthi, “Intelligent Meta-Heuristic-Based Optimization of Traffic Light Timing Using Artificial Intelligence Techniques,” Electronics (Basel), vol. 12, no. 24, p. 4968, Dec. 2023. https://doi.org/10.3390/electronics12244968