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http://dx.doi.org/10.12815/kits.2019.18.6.110

A Study on Estimation of Traffic Flow Using Image-based Vehicle Identification Technology  

Kim, Minjeong (Dept. of Urban Planning and Engineering, Dong-A University)
Jeong, Daehan (Dept. of Urban Planning and Engineering, Dong-A University)
Kim, Hoe Kyoung (Dept. of Urban Planning and Engineering, Dong-A University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.18, no.6, 2019 , pp. 110-123 More about this Journal
Abstract
Traffic data is the most basic element necessary for transportation planning and traffic system operation. Recently, a method of estimating traffic flow characteristics using distance to a leading vehicle measured by an ADAS camera has been attempted. This study investigated the feasibility of the ADAS vehicle reflecting the distance error of image-based vehicle identification technology as a means to estimate the traffic flow through the normalized root mean square error (NRMSE) based on the number of lanes, traffic demand, penetration rate of probe vehicle, and time-space estimation area by employing the microscopic simulation model, VISSIM. As a result, the estimate of low density traffic flow (i.e., LOS A, LOS B) is unreliable due to the limitation of the maximum identification distance of ADAS camera. Although the reliability of the estimates can be improved if multiple lanes, high traffic demands, and high penetration rates are implemented, artificially raising the penetration rates is unrealistic. Their reliability can be improved by extending the time dimension of the estimation area as well, but the most influential one is the driving behavior of the ADAS vehicle. In conclusion, although it is not possible to accurately estimate the traffic flow with the ADAS camera, its applicability will be expanded by improving its performance and functions.
Keywords
Advanced driver assistance system; Image-based vehicle identification; Probe vehicle; Market penetration rate; Normalized root mean square error;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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