Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2022

Abstract

Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of background occlusion. The result shows that the proposed algorithm can identify traffic flow and turning events at a root mean square error (RMSE) of 10. The result shows that a Kalman filter with an intersection of union (IOU)-based tracker performs well at the condition of background occlusion. Also, the proposed algorithm can detect and track vehicles at different optical conditions. Bad weather and night-time will influence the detecting and tracking process in areas far from traffic cameras. The traffic flow extracted from traffic videos contains road information, so it can not only help with single intersection control, but also provides information for a road network. The temporal characteristic of observed traffic flow gives the potential to predict traffic flow based on detected traffic flow, which will make the traffic light control more efficient.

Keywords

data and data science, computer vision, intelligent traffic system, sensor data analytics, traffic information, vehicle detection

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Integrative Research Areas

Publication

Transportation Research Record

Volume

2676

Issue

7

First Page

55

Last Page

65

ISSN

0361-1981

Identifier

10.1177/03611981221076426

Publisher

SAGE Publications

Additional URL

https://doi.org/10.1177/03611981221076426

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