MTGCN: A Multitask Deep Learning Model for Traffic Flow Prediction
The prediction of traffic flow is of great importance to urban planning and intelligent transportation systems. Recently, deep learning models have been applied to study this problem. However, there still exist two main limitations: (1) They do not effect
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Yunmook Nah · Bin Cui · Sang-Won Lee · Jeffrey Xu Yu · Yang-Sae Moon · Steven Euijong Whang (Eds.)
Database Systems for Advanced Applications 25th International Conference, DASFAA 2020 Jeju, South Korea, September 24–27, 2020 Proceedings, Part I
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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More information about this series at http://www.springer.com/series/7409
Yunmook Nah Bin Cui Sang-Won Lee Jeffrey Xu Yu Yang-Sae Moon Steven Euijong Whang (Eds.) •
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Database Systems for Advanced Applications 25th International Conference, DASFAA 2020 Jeju, South Korea, September 24–27, 2020 Proceedings, Part I
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Editors Yunmook Nah Dankook University Yongin, Korea (Republic of) Sang-Won Lee Sungkyunkwan University Suwon, Korea (Republic of) Yang-Sae Moon Kangwon National University Chunchon, Korea (Republic of)
Bin Cui Peking University Haidian, China Jeffrey Xu Yu Department of System Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong, Hong Kong Steven Euijong Whang Korea Advanced Institute of Science and Technology Daejeon, Korea (Republic of)
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-59409-1 ISBN 978-3-030-59410-7 (eBook) https://doi.org/10.1007/978-3-030-59410-7 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is pu