Prediction method of train delay using deep learning technique

被引：0

作者：

Tatsui D. ^{[1
]}

Nakabasami K. ^{[1
]}

Kunimatsu T. ^{[1
]}

Sakaguchi T. ^{[1
]}

Tanaka S. ^{[1
]}

机构：

[1] Transport Operation Systems Laboratory, Signalling and Transport Information Technology Division

来源：

Quarterly Report of RTRI (Railway Technical Research Institute) | 2021年 / 62卷 / 04期

关键词：

Deep learning; LSTM; Traffic operation arrangement; Train delay prediction;

D O I：

10.2219/rtriqr.62.4_263

中图分类号：

学科分类号：

摘要：

Commuter lines in metropolitan areas in Japan suffer frequent short train delays. Train dispatchers reschedule operations according to these delays and how they evolve. However, because of the complex way in which they evolve, it is difficult to predict delays of up to a few tens of minutes. To build an accurate prediction method, we developed prediction method using a deep learning model called Long Short Term Memory. This paper reports on the prediction performance of this method compared with the conventional method using neural networks. © 2021 Ken-yusha Inc.. All rights reserved.

引用

页码：263 / 268

页数：5

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