Deep Learning–Driven Traffic Detection and Flow Optimization using Simulation-Based Analysis in Spatial Domain

The paper examined an entire system for recognizing traffic and optimizing green signal timing with artificial video data as actual videos were difficult to utilize. This research employed pre-processing, feature extraction, YOLO detection, and then simulated signal controls to determine waiting times. Results demonstrated improvements in detection accuracy and queue reductions during heavy traffic periods. The system may not be optimal; however, it ran consistently well throughout the majority of the test scenarios. In addition to demonstrating the feasibility of this approach (combining machine learning, deep learning, and simulation) to develop a traffic management concept which performs better than traditional fixed-time traffic signal systems, this paper also aimed at creating a relatively simple methodology so that it could run on low-end hardware configurations.