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http://dx.doi.org/10.12815/kits.2020.19.1.32

A study on Estimating the Transfer Time of Transit Users Using Deep Neural Network Models  

Lee, Gyeongjae (Dept. of Urban Planning, Hongik University)
Kim, Sujae (Dept. of Urban Planning, Hongik University)
Moon, Hyungtaek (Dept. of Urban Planning, Hongik University)
Han, Jaeyoon (Dept. of Urban Planning, Hongik University)
Choo, Sangho (Dept. of Urban Planning, Hongik University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.19, no.1, 2020 , pp. 32-43 More about this Journal
Abstract
The transfer time is an important factor in establishing public transportation planning and policy. Therefore, in this study, the influencing factors of the transfer time for transit users were identified using smart card data, and the estimation results for the transfer time using the deep learning method such as deep neural network models were compared with traditional regression models. First, the intervals and the distance to the bus stop had positive effects on the subway-to-bus transfer time, and the number of bus routes had a negative effect. This also showed that the transfer time is affected by the area in which the subway station exists. Based on the influencing factors of the transfer time, the deep learning models were developed and their estimation results were compared with the regression model. For model performance, the deep learning models were better than those of the regression models. These results can be used as basic data for transfer policies such as the differential application of transit allowance times according to region.
Keywords
Transfer Time; Deep Learning; Deep Neural Network; Regression; Smart Card Data;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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