Automating Traffic Flow and Incident Detection Using Computer Vision in Smart City Transport Systems

Authors

Ravikanth Konda

Abstract

The rapid growth of urbanization has turned traffic congestion and road safety into pressing issues in cities globally. Conventional traffic management systems based on legacy infrastructure, such as inductive loop detectors and fixed cameras, are basically reactive, need human intervention, and are not adaptable enough to cope with dynamic traffic scenarios. Computer vision, powered by deep learning, is an intelligent, scalable, and data-driven method for managing road networks.
This paper describes an integrated framework of smart city traffic management with the use of computer vision and machine learning algorithms for automating traffic flow analysis and real-time incident detection. The system applies cutting-edge object detection models like YOLOv3 and Faster R-CNN to detect vehicles, pedestrians, and obstacles, while multi-object tracking methods like DeepSORT aid in movement pattern analysis across frames. Time-series forecasting and anomaly detection algorithms detect events like abrupt stops, wrong-way driving, or traffic congestion suggestive of an accident.
We suggest a modular architecture with edge computing for low-latency tasks and cloud platforms for big data analytics. We test the system using publicly available datasets like UA-DETRAC, CityFlow, and LISA Traffic and observe notable performance improvements in response time (45% less time taken), accuracy of incident detection (92.3% F1-score), and improved traffic flow efficiency by 30% through adaptive signaling. We also tackle challenges that come with privacy, scalability, and integration with current infrastructure.
This study highlights how computer vision powered by AI can transform traffic management in smart cities through proactive incident detection, real-time traffic analysis, and data-driven decision-making for urban mobility.

Keywords

Automating Traffic Flow and Incident Detection Using Computer Vision in Smart City Transport Systems. Ravikanth Konda. 2020. IJIRCT, Volume 6, Issue 1. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2505013