JACIII Vol.22 No.5 pp. 704-710
doi: 10.20965/jaciii.2018.p0704


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

Robert Kerwin C. Billones, Argel A. Bandala, Laurence A. Gan Lim, Edwin Sybingco, Alexis M. Fillone, and Elmer P. Dadios

De La Salle University
2401 Taft Avenue, Manila 0922, Philippines

March 13, 2018
June 15, 2018
September 20, 2018
traffic scene analysis, intelligent transport systems, computer vision, vehicle detection and tracking, microscopic traffic flow modelling

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.

Cite this article as:
R. Billones, A. Bandala, L. Lim, E. Sybingco, A. Fillone, and E. Dadios, “Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 704-710, 2018.
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Last updated on Oct. 23, 2018