Short-term Traffic Flow Prediction with Up-and-Downstream Spatial-temporal Relationship based on LSTM

カテゴリ: 部門大会

論文No: SS3-10

グループ名: 【C】2019年電気学会電子・情報・システム部門大会プログラム

発行日: 2019/08/28

タイトル(英語): Short-term Traffic Flow Prediction with Up-and-Downstream Spatial-temporal Relationship based on LSTM

著者名: Yang Kaijie(早稲田大学),Song Wen(早稲田大学),Fujimura Shigeru(早稲田大学)

著者名(英語): Kaijie Yang|Wen Song|Shigeru Fujimura

キーワード: traffic flow prediction|long short-term memory|spatial-temporal relationship|Intelligent Transportation System

要約(日本語): Accurate short-term traffic flow prediction is an integral part of Intelligent Transportation System (ITS). There are many time series forecasting methods applied to traffic flow prediction. However, many methods only focus on the time series data of traffic flow at a single observation position without considering the spatial characteristics of traffic flow. In order to solve this problem, this paper proposes a method with up-and-downstream spatial-temporal relationship based on LSTM. The results show that the proposed method can combine temporal information and spatial information of traffic flow to improve the accuracy of prediction.

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