Citation

Ibryaeva OL, Shepelev VD, Kuzmicheva OD, Аlmetova ZV, Zhulev AE, Cherpakov AO. Transp. Res. Proc. 2021; 52: 589-596.

Copyright

(Copyright © 2021, Elsevier Publications)

DOI

10.1016/j.trpro.2021.01.070

PMID

unavailable

Abstract

The article deals with the development of a computer system, which allows us to recognize vehicles, track them, and measure the time needed to cross an intersection by each car in the lane. The main area of research is the analysis of the dependence of the intersection crossing time on the position of vehicles in the queue formed in the traffic lane. To count the vehicles in the queue and determine their category, we used the Yolo v3 neural network and the SORT tracker modified to return the object class. The article describes in detail the proposed algorithm for collecting the data on the queue of vehicles: the number of vehicles in the queue and their classes, the time of passing the stop line and crossing the intersection, as well as determining the driving direction. All vehicles are divided into three categories depending on their acceleration. We analyzed the collected data on the queue structure and the time of its unloading and demonstrated their direct interconnection.

23rd EURO Working Group on Transportation Meeting, EWGT 2020, 16-18 September 2020, Paphos, Cyprus

Language: en

Keywords

computer vision; intersection; motor vehicles; traffic count; vehicle queue