Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling

Abstract

This paper presents the development of a vision-based system for microscopic road traffic scene analysis and understanding using computer vision and computational intelligence techniques. The traffic flow model is calibrated using the information obtained from the road-side cameras. It aims to demonstrate an understanding of different levels of traffic scene analysis from simple detection, tracking, and classification of traffic agents to a higher level of vehicular and pedestrian dynamics, traffic congestion build-up, and multi-agent interactions. The study used a video dataset suitable for analysis of a T-intersection. Vehicle detection and tracking have 88.84% accuracy and 88.20% precision. The system can classify private cars, public utility vehicles, buses, and motorcycles. Vehicular flow of every detected vehicles from origin to destination are also monitored for traffic volume estimation, and volume distribution analysis. Lastly, a microscopic traffic model for a T-intersection was developed to simulate a traffic response based on actual road scenarios.

Image recognition for vehicle traffic analysis at intersections

“…As a similar research example, there is a study that recognizes the front part of a vehicle and counts the number of vehicles (Billones et al 2018;Teknomo 2016). There is also research on vehicle recognition centering on images taken from the sky of unmanned aerial vehicles (UAV) (Fujimoto et al 2014;Ammour et al 2017).…”