Traffic Prediction During Large-scale Events Based on Pattern-aware Regression

• Item type: Journal(1)
• 公開日: 2022-01-15
• タイトル: Traffic Prediction During Large-scale Events Based on Pattern-aware Regression
• タイトル / 言語: en
• タイトル: Traffic Prediction During Large-scale Events Based on Pattern-aware Regression
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:ニューノーマル時代の高度交通システムとパーベイシブシステム] traffic prediction, large-scale event, non-negative matrix factorization, deep Bayesian learning
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Systems and Information Engineering, University of Tsukuba／National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): Graduate School of Systems and Information Engineering, University of Tsukuba / National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology (AIST)
• 著者名: Takafumi, Okukubo; Yoshiaki, Bando; Masaki, Onishi
• 著者名(英): Takafumi, Okukubo; Yoshiaki, Bando; Masaki, Onishi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper presents a statistical method combined with a neural network for efficient traffic prediction from a limited amount of training data. The traffic prediction during a large-scale event is essential to maintain the safety of event participants. The conventional methods for predicting traffic time series, however, cannot be utilized because the rare nature of the large-scale events prevents us from preparing a sufficient amount of training data. To efficiently train traffic prediction from a limited amount of training data, we propose a pattern-aware regression method that reduces the number of model parameters by interpreting traffic data as a weighted sum of latent behavior patterns. The proposed method trains a neural regression model to predict the weights of these patterns from the event information instead of directly predicting the traffic time series. The behavior patterns are jointly estimated during the training in a Bayesian manner to avoid overfitting. We performed experiments with foot traffic data recorded at a real soccer stadium and show that the proposed method outperforms the conventional direct regression methods. We also demonstrate an application of our method for predicting travel time from the stadium to the nearest highway interchange, which outperforms a popular commercial service.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.42; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper presents a statistical method combined with a neural network for efficient traffic prediction from a limited amount of training data. The traffic prediction during a large-scale event is essential to maintain the safety of event participants. The conventional methods for predicting traffic time series, however, cannot be utilized because the rare nature of the large-scale events prevents us from preparing a sufficient amount of training data. To efficiently train traffic prediction from a limited amount of training data, we propose a pattern-aware regression method that reduces the number of model parameters by interpreting traffic data as a weighted sum of latent behavior patterns. The proposed method trains a neural regression model to predict the weights of these patterns from the event information instead of directly predicting the traffic time series. The behavior patterns are jointly estimated during the training in a Bayesian manner to avoid overfitting. We performed experiments with foot traffic data recorded at a real soccer stadium and show that the proposed method outperforms the conventional direct regression methods. We also demonstrate an application of our method for predicting travel time from the stadium to the nearest highway interchange, which outperforms a popular commercial service.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.42; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 63, 号 1, 発行日 2022-01-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764