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http://dx.doi.org/10.12815/kits.2018.17.2.39

Development of Vehicle Queue Length Estimation Model Using Deep Learning  

Lee, Yong-Ju (Dept. of Transportation Research Institute, Univ. of Ajou)
Hwang, Jae-Seong (Dept. of Construction and Transportation Eng., Univ. of Ajou)
Kim, Soo-Hee (Dept. of Research Institute, Korea Expressway Corporation)
Lee, Choul-Ki (Dept. of Transportation System Eng., Univ. of Ajou)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.2, 2018 , pp. 39-57 More about this Journal
Abstract
The purpose of this study was to construct an artificial intelligence model that learns and estimates the relationship between vehicle queue length and link travel time in urban areas. The vehicle queue length estimation model is modeled by three models. First of all, classify whether vehicle queue is a link overflow and estimate the vehicle queue length in the link overflow and non-overflow situations. Deep learning model is implemented as Tensorflow. All models are based DNN structure, and network structure which shows minimum error after learning and testing is selected by diversifying hidden layer and node number. The accuracy of the vehicle queue link overflow classification model was 98%, and the error of the vehicle queue estimation model in case of non-overflow and overflow situation was less than 15% and less than 5%, respectively. The average error per link was about 12%. Compared with the detecting data-based method, the error was reduced by about 39%.
Keywords
Deep Learning; Big Data; Vehicle Queue Length; Link Travel Time;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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