Microscopic road traffic scene analysis using computer vision and traffic flow modelling

• Main Authors: Billones, Robert Kerwin C., Bandala, Argel A., Gan Lim, Laurence A., Sybingco, Edwin, Fillone, Alexis M., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2018
• Subjects: Traffic flow; Traffic congestion; Computer vision; Intelligent transportation systems; Mechanical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2933
• Institution: De La Salle University

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 multiagent 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. © 2018 Fuji Technology Press.All Rights Reserved.