Working Paper Article Version 2 This version is not peer-reviewed

Estimation of Lane-Level Traffic Flow by Using Deep Learning Technique

Version 1 : Received: 28 May 2021 / Approved: 31 May 2021 / Online: 31 May 2021 (13:20:23 CEST)
Version 2 : Received: 1 June 2021 / Approved: 1 June 2021 / Online: 1 June 2021 (14:42:58 CEST)

A peer-reviewed article of this Preprint also exists.

Liu, C.-M.; Juang, J.-C. Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique. Appl. Sci. 2021, 11, 5619. Liu, C.-M.; Juang, J.-C. Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique. Appl. Sci. 2021, 11, 5619.

Journal reference: Appl. Sci. 2021, 11, 5619
DOI: 10.3390/app11125619

Abstract

This paper proposes a neural network which fuses the data received from a camera system on a gantry, to detect moving objects and calculate relative position and velocity of the vehicles traveling on a freeway, this information is used to estimate the traffic flow. To estimate the traffic flow at both microscopic and macroscopic view, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking, then counting the number of vehicles pass through the freeway by drawing virtual lines and hot zones, also counting the velocity of each vehicles. The information is then pass to the traffic control center, in order to monitoring and control traffic flow on freeways, and analyzing freeway conditions.

Subject Areas

traffic flow; object detection; object tracking; deep learning

Comments (1)

Comment 1
Received: 1 June 2021
Commenter: Chieh-Min Liu
Commenter's Conflict of Interests: Author
Comment: Change the whole newly-revised manuscript.
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