Computer Vision Analysis for Traffic Monitoring and Road Safety in Smart City Concept
DOI:
https://doi.org/10.62872/vk562576Keywords:
Computer vision, Intelligent transportation system, Road safety, Smart city, Traffic monitoringAbstract
Rapid urban growth and rising traffic complexity require Smart City solutions that move beyond passive CCTV toward intelligent, real-time traffic management. This study examines how computer vision–based analytics contribute to road safety when integrated into an Intelligent Transportation System (ITS). A quantitative quasi-experimental design was applied across multiple intersections using a 12-month before–after window. Data were collected from video analytics (vehicle and pedestrian detection, tracking, violations, road conditions), adaptive signal logs, crash and injury records, near-miss indicators, and contextual variables such as weather and traffic volume. Analysis combined perception validation (mAP, tracking accuracy), time-series operational assessment, and Difference-in-Differences modeling to estimate safety impacts. Results show high perception reliability (mAP > 0.85) and significant operational improvements, including a 33% reduction in waiting time and 35% shorter queues. More importantly, red-light violations decreased by 39%, near-miss events by 45%, crash frequency by 42%, and severity index by 37%. The findings indicate a causal pathway from vision-based perception to adaptive control and enforcement, leading to measurable safety gains. The study concludes that computer vision serves as a safety governance instrument within Smart City ITS when detection outputs are tightly coupled with intervention mechanisms.
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Adewopo, V., & Elsayed, N. (2023). Smart city transportation: Deep learning ensemble approach for traffic accident detection. IEEE Access, 12, 59134–59147. https://doi.org/10.1109/access.2024.3387972
Adewopo, V., Elsayed, N., ElSayed, Z., Ozer, M., Zekios, C., Abdelgawad, A., & Bayoumi, M. (2024). Big data and deep learning in smart cities: A comprehensive dataset for AI-driven traffic accident detection and computer vision systems. 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI), 1–6. https://doi.org/10.1109/icmi60790.2024.10586073
Aldoski, Z., & Koren, C. (2025). Traffic sign detection and quality assessment using YOLOv8 in daytime and nighttime conditions. Sensors, 25. https://doi.org/10.3390/s25041027
Almaliki, M., Bamaqa, A., Badawy, M., Farrag, T., Balaha, H., & Elhosseini, M. (2025). Adaptive traffic light management for mobility and accessibility in smart cities. Sustainability. https://doi.org/10.3390/su17146462
Balasaranya, K., Chandravathi, C., Balamurugan, M., Logapriyadarshini, K., Babu, S., & Naganathan, T. (2025). Real-time traffic violation detection with automated enforcement using computer vision. 2025 11th ICCSP, 361–366. https://doi.org/10.1109/iccsp64183.2025.11089113
C, B. (2025). AI-powered smart traffic management system. International Journal of Scientific Research in Engineering and Management. https://doi.org/10.55041/ijsrem47454
Chebykin, I. (2021). Automating road traffic monitoring using computer vision. World of Transport and Transportation. https://doi.org/10.30932/1992-3252-2020-18-6-74-87
Chen, C. (2024). Intelligent traffic monitoring and management based on computer vision technology. 2024 ICDSIS, 1–5. https://doi.org/10.1109/icdsis61070.2024.10594478
Dilek, E., & Dener, M. (2023). Computer vision applications in intelligent transportation systems: A survey. Sensors, 23. https://doi.org/10.3390/s23062938
DolatAbadi, S., Hashemi, S., Hosseini, M., & AliHosseini, M. (2025). Revolutionizing traffic management with AI-powered machine vision: A step toward smart cities. arXiv. https://doi.org/10.48550/arxiv.2503.02967
Hamza, S., E., & Rathod, V. (2025). AI driven urban planning for real time traffic monitoring framework using OpenCV and YOLO. Journal of Information Systems Engineering and Management. https://doi.org/10.52783/jisem.v10i33s.5765
Jiang, J., Xu, G., Wang, H., Yang, Z., Sun, B., Guan, C., Feng, J., Y., & Chen, X. (2024). High-accuracy road surface condition detection through multi-sensor information fusion. Sensors and Actuators A. https://doi.org/10.1016/j.sna.2024.115829
K, P., R.K., P., R, B., L, R., & Sivaraju, S. (2025). Real time implementation of computer vision based road traffic monitoring system using YOLO 11. 2025 ICIRCA, 1994–1999. https://doi.org/10.1109/icirca65293.2025.11089681
Khan, H., & Thakur, J. (2024). Smart traffic control: Machine learning for dynamic road traffic management. Multimedia Tools and Applications, 84, 10321–10345. https://doi.org/10.1007/s11042-024-19331-4
Konda, R. (2024). AI and computer vision applications in smart cities for enhanced security and traffic management. Journal of Advances in Developmental Research. https://doi.org/10.71097/ijaidr.v15.i1.1408
Kumar, E., Bhavishya, A., Pranava, G., Reddy, K., & S. (2025). Lane-wise traffic intelligence using deep vision systems for signal optimization. IRJAEH. https://doi.org/10.47392/irjaeh.2025.0328
Lin, T., & Lin, R. (2025). Smart city traffic flow and signal optimization using STGCN-LSTM and PPO algorithms. IEEE Access, 13, 15062–15078. https://doi.org/10.1109/access.2024.3519512
M, P. (2025). Traffic vision: AI-powered traffic monitoring system and signal optimization. IJRASET. https://doi.org/10.22214/ijraset.2025.68829
Medina-Salgado, B., Sánchez-Delacruz, E., Del Pilar Pozos Parra, M., & Sierra, J. (2022). Urban traffic flow prediction techniques: A review. Sustainable Computing, 35, 100739. https://doi.org/10.1016/j.suscom.2022.100739
Qiu, M., Mao, S., Zhu, J., & Yang, Y. (2025). Spatiotemporal multi-feature fusion vehicle trajectory anomaly detection. Accident Analysis & Prevention, 211, 107911. https://doi.org/10.1016/j.aap.2024.107911
Ramana, K., Srivastava, G., Kumar, M., Gadekallu, T., Lin, J., Alazab, M., & Iwendi, C. (2023). Vision transformer approach for traffic congestion prediction. IEEE T-ITS, 24, 3922–3934. https://doi.org/10.1109/tits.2022.3233801
Rathee, M., Bačić, B., & Doborjeh, M. (2023). Automated road defect and anomaly detection for traffic safety: A systematic review. Sensors, 23. https://doi.org/10.3390/s23125656
Saleem, M., Abbas, S., Ghazal, T., Khan, M., Sahawneh, N., & Ahmad, M. (2022). Fusion-based intelligent traffic congestion control system. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2022.03.003
Skoropad, V., Deđanski, S., Pantović, V., Injac, Z., Vujičić, S., Jovanović-Milenković, M., Jevtić, B., Lukić-Vujadinović, V., Vidojević, D., & Bodolo, I. (2025). Dynamic traffic flow optimization using reinforcement learning. Sustainability. https://doi.org/10.3390/su17083383
Thompson, J. (2025). AI-integrated IoT networks for smart city traffic management. IJICSITR. https://doi.org/10.63665/ijicsitr.v1i02.01
Ubaid, M., Saba, T., Draz, H., Rehman, A., Ghani, M., & Kolivand, H. (2022). Intelligent traffic signal automation based on computer vision. IT Professional, 24, 27–33. https://doi.org/10.1109/mitp.2021.3121804
Vyas, A., & Patel, P. (2025). Real-time traffic surveillance and vehicle speed detection using machine vision. Journal of Information Systems Engineering and Management. https://doi.org/10.52783/jisem.v10i42s.7890
Yue, H. (2024). Influence of streetscape characteristics on pedestrian crashes using street view images. Accident Analysis & Prevention, 205, 107693. https://doi.org/10.1016/j.aap.2024.107693
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Copyright (c) 2026 Luki Ishwara, Hasbu Naim Syaddad, Andi Agus Salim, Bobi Kurniawan, Adam Mukharil Bachtiar, Ednawati Rainarli (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
