2026-06-16T12:11:56Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00215830 2025-01-19T16:00:42Z 581:10784:10785

Traffic Prediction During Large-scale Events Based on Pattern-aware Regression Traffic Prediction During Large-scale Events Based on Pattern-aware Regression Takafumi, Okukubo Yoshiaki, Bando Masaki, Onishi Takafumi, Okukubo Yoshiaki, Bando Masaki, Onishi [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 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 ------------------------------ 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 ------------------------------ 2022-01-15 eng journal article https://ipsj.ixsq.nii.ac.jp/records/215830 1882-7764 AN00116647 情報処理学会論文誌 63 1 https://ipsj.ixsq.nii.ac.jp/record/215830/files/IPSJ-JNL6301020.pdf application/pdf 1.5 MB 2024-01-15