地形の影響を考慮した積雪時のバス遅延の分析と到着時刻予測への適用

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2020/02/01

タイトル(英語): An Analysis of Bus Traffic Delay with Geographical Characteristics and an Application to Arrival Time Prediction in Snowfall Situation

著者名: 廣井　慧（名古屋大学大学院工学研究科），花之内　広太郎（名古屋大学大学院工学研究科），今井　瞳（名古屋大学大学院工学研究科），河口　信夫（名古屋大学大学院工学研究科／名古屋大学未来社会創造機構）

著者名(英語): Kei Hiroi (Graduate School of Engineering, Nagoya University), Koutaro Hananouchi (Graduate School of Engineering, Nagoya University), Hitomi Imai (Graduate School of Engineering, Nagoya University), Nobuo Kawaguchi (Graduate School of Engineering, Nagoya University/Institutes of Innovation for Future Society, Nagoya University)

キーワード: バスロケーションシステム，重回帰分析，カルマンフィルタ，遅延予測，交通障害　　bus location system，regression analysis，kalman filter，delay prediction，traffic bottleneck

要約(英語): Bus transportation service is more influenced than other public transport by various factors such as traffic congestion, weather condition, number of passengers, traffic signals. These factors often cause delay and the users may feel inconvenience while waiting at the bus stop. In the case of snowfall event, a large delay occurs, which greatly reduces the convenience of the bus. This paper aims at highly accurate arrival time prediction for each bus stop section in snow event in urban area. We investigate vulnerability of bus operations to snowfall and incorporate into predictions using geographical characteristics. In each bus stop section, we estimate geographical characteristics (gradient angle and gradient direction) and snow accumulation amount with detailed spatial resolution as factors affecting bus delay. Then, we evaluate a prediction accuracy using the arrival time prediction model with multiple regression analysis and the Kalman filter. As a result of the multiple regression analysis, it was found that the geographical characteristics of each bus stop section were the explanatory variables that greatly affect the bus delay at snowfall event. Furthermore, we predicted the bus arrival time using actual bus operation data. Of the 29 routes, 18 routes showed improvement in the predicted arrival time.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.2 （2020）　特集：エネルギーデータを対象としたIoT，AI活用技術

本誌掲載ページ: 257-266 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/2/140_257/_article/-char/ja/