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http://dx.doi.org/10.9708/jksci.2022.27.08.069

Predicting lane speeds from link speeds by using neural networks  

Pyun, Dong hyun (Dept. of Computer Engineering, Graduate School, Hongik University)
Pyo, Changwoo (Dept. of Computer Engineering, Hongik University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.8, 2022 , pp. 69-75 More about this Journal
Abstract
In this paper, a method for predicting the speed for each lane from the link speed using an artificial neural network is presented to increase the accuracy of predicting the required time of a driving route. The time required for passing through a link is observed differently depending on the direction of going straight, turning right, or turning left at the intersection of the end of the link. Therefore, it is necessary to predict the speed according to the vehicle's traveling direction. Data required for learning and verification were constructed by refining the data measured at the Gongpyeong intersection of Gukchaebosang-ro in Daegu Metropolitan City and four adjacent intersections around it. Five neural network models were used. In addition, error analysis of the prediction was performed to select a neural network experimentally suitable for the research purpose. Experimental results showed that the error in the estimation of the time required for each lane decreased by 17.4% for the straight lane, 4.4% for the right-turn lane, and 3.9% for the left-turn lane. This experiment is the result of analyzing only one link. If the entire pathway is tested, the effect is expected to be greater.
Keywords
ITS; estimate the transit time; AI; route analysis; lane speeds;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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