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http://dx.doi.org/10.12815/kits.2018.17.4.54

Study of the Operation of Actuated signal control Based on Vehicle Queue Length estimated by Deep Learning  

Lee, Yong-Ju (Dept. of Transportation Research Institute, Univ. of Ajou)
Sim, Min-Gyeong (Dept. of Transportation Eng., Univ. of Ajou)
Kim, Yong-Man (Dept. of Road Equipment, KoROAD)
Lee, Sang-Su (Dept. of Transportation System Eng., Univ. of Ajou)
Lee, Cheol-Gi (Dept. of Transportation System Eng., Univ. of Ajou)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.4, 2018 , pp. 54-62 More about this Journal
Abstract
As a part of realization of artificial intelligence signal(AI Signal), this study proposed an actuated signal algorithm based on vehicle queue length that estimates in real time by deep learning. In order to implement the algorithm, we built an API(COM Interface) to control the micro traffic simulator Vissim in the tensorflow that implements the deep learning model. In Vissim, when the link travel time and the traffic volume collected by signal cycle are transferred to the tensorflow, the vehicle queue length is estimated by the deep learning model. The signal time is calculated based on the vehicle queue length, and the simulation is performed by adjusting the signaling inside Vissim. The algorithm developed in this study is analyzed that the vehicle delay is reduced by about 5% compared to the current TOD mode. It is applied to only one intersection in the network and its effect is limited. Future study is proposed to expand the space such as corridor control or network control using this algorithm.
Keywords
AI Signal; Deep Learning; Vehicle Queue Length; Vissim COM Interface; Tensorflow;
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