A Method for Improving Traffic Management Based on Computer Vision and Traffic Simulation

Abstract

This study develops an integrated framework combining computer vision and traffic simulation for optimizing traffic management in high-density urban commercial areas. The framework employs a two-phase data acquisition approach: UAV-captured video is first processed using an enhanced YOLOv10 algorithm to identify critical road segments and key intersections, followed by handheld video recordings at target intersections for extracting dynamic traffic parameters (including vehicle counts, speeds, pedestrian density, and mixed-traffic interactions). The proposed framework was applied to the Xianlie East Road-Lianquan Road intersection in Guangzhou, a conflict-prone hub adjacent to densely clustered garment wholesale markets. Key improvement measures evaluated include crosswalk relocation and non-motorized lane adjustments. Additionally, the signal cycle duration was extended from 80 to 90 seconds to alleviate phase transition conflicts. Simulation inputs integrate field-observed behavioral patterns (e.g., 83% pedestrian compliance with signals). Results demonstrate significant improvements: 16.7% reduction in eastbound queue lengths (240.23m), 80.4% decrease in vehicle delays (56.46s), 44.8% shorter travel times (51.61s), and 83% fewer pedestrian-vehicle conflicts. This approach provides a scalable technical pathway for adaptive traffic governance in complex urban environments.