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Technol."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this article, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: (1) cST-ML captures the dynamics of traffic prediction tasks using variational inference, and to better capture the temporal uncertainties within tasks, cST-ML performs as a rolling window within each task; (2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic-related features; (3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models especially when obvious traffic dynamics and temporal uncertainties are presented.<\/jats:p>","DOI":"10.1145\/3474837","type":"journal-article","created":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T15:07:50Z","timestamp":1641395270000},"page":"1-19","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Urban Traffic Dynamics Prediction\u2014A Continuous Spatial-temporal Meta-learning Approach"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0947-1875","authenticated-orcid":false,"given":"Yingxue","family":"Zhang","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8972-503X","authenticated-orcid":false,"given":"Yanhua","family":"Li","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4930-6572","authenticated-orcid":false,"given":"Xun","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Iowa, Iowa City, IA, USA"}]},{"given":"Jun","family":"Luo","sequence":"additional","affiliation":[{"name":"Lenovo Group Limited, Quarry Bay, Hong Kong"}]},{"given":"Zhi-Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Minnesota-Twin Cities, Minneapolis, MN, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,1,5]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"https:\/\/github.com\/cST-ML\/cST-ML 2020 cST-ML"},{"key":"e_1_3_2_3_2","unstructured":"https:\/\/github.com\/Jackson-Kang\/Pytorch-VAE-tutorial 2020 VAE pyTorch Tutorial"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1498-8_58"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397536.3422261"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.07.069"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157340"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2016.01.234"},{"key":"e_1_3_2_9_2","volume-title":"Proceedings of the 6th International Workshop on Urban Computing","author":"Cui Zhiyong","year":"2017","unstructured":"Zhiyong Cui, Ruimin Ke, and Yinhai Wang. 2017. 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