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http://dx.doi.org/10.12815/kits.2021.20.6.26

Predicting a Queue Length Using a Deep Learning Model at Signalized Intersections  

Na, Da-Hyuk (Ajou Transp. Research Institute)
Lee, Sang-Soo (Dept. of Transportation Eng., Ajou Univ.)
Cho, Keun-Min (Ajou Transp. Research Institute)
Kim, Ho-Yeon (Dept. of Transportation Eng., Ajou Univ.)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.20, no.6, 2021 , pp. 26-36 More about this Journal
Abstract
In this study, a deep learning model for predicting the queue length was developed using the information collected from the image detector. Then, a multiple regression analysis model, a statistical technique, was derived and compared using two indices of mean absolute error(MAE) and root mean square error(RMSE). From the results of multiple regression analysis, time, day of the week, occupancy, and bus traffic were found to be statistically significant variables. Occupancy showed the most strong impact on the queue length among the variables. For the optimal deep learning model, 4 hidden layers and 6 lookback were determined, and MAE and RMSE were 6.34 and 8.99. As a result of evaluating the two models, the MAE of the multiple regression model and the deep learning model were 13.65 and 6.44, respectively, and the RMSE were 19.10 and 9.11, respectively. The deep learning model reduced the MAE by 52.8% and the RMSE by 52.3% compared to the multiple regression model.
Keywords
Multiple Regression; Deep Learning; LSTM; Queue Length; Forecasting;
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