Big Data and Mobility as a Service -

Big Data and Mobility as a Service

Buch | Softcover
306 Seiten
2021
Elsevier - Health Sciences Division (Verlag)
978-0-323-90169-7 (ISBN)
119,65 inkl. MwSt
Big Data and Mobility as a Service explores MaaS platforms that can be adaptable to the ever-evolving mobility environment. It looks at multi-mode urban crowd data to assess urban mobility characteristics, their shared transportation potential, and their performance conditions and constraints. The book analyzes the roles of multimodality, travel behavior, urban mobility dynamics and participation. Combined with insights on using big data to analyze market and policy decisions, this book is an essential tool for urban transportation management researchers and practitioners.

Haoran (Ronan) Zhang is Assistant Professor in the Center for Spatial Information Science at the University of Tokyo, a Researcher at the School of Business Society and Engineering at Mälardalen University in Sweden, and Senior Scientist at Locationmind Inc. in Japan. His research includes smart supply chain technologies, GPS data in shared transportation, urban sustainable performance, GIS technologies in renewable energy systems, and smart cities. He is author of numerous journal articles and Editorial Board Member of several international academic journals. He has Ph.D.’s in both Engineering and Sociocultural Environment and was awarded Excellent Young Researcher by Japan’s Ministry of Education, Culture, Sports, Science and Technology. Xuan Song is currently an Excellent Young Researcher of Japan MEXT, and an Associate Professor at The University of Tokyo. He received the Ph.D. degree in signal and information processing from Peking University, China, in 2010. From 2010 to 2012, he worked in Center for Spatial Information Science, The University of Tokyo as a post-doctoral researcher. From 2012 to 2015, he worked in Center for Spatial Information Science, The University of Tokyo as a Project Assistant Professor. In 2015, he was promoted to Project Associate Professor with the Center for Spatial Information Science, The University of Tokyo. In 2018, he joined in Artificial Intelligence Research Center (AIRC) of AIST as a tenured Senior Researcher. In the past five years, he led and participated in many important projects as principal investigator or primary actor in Japan, such as DIAS/GRENE Grant of MEXT, Japan; Japan/US Big Data and Disaster Project of JST, Japan; Young Scientists Grant and Scientific Research Grant of MEXT, Japan; Research Grant of MLIT, Japan; CORE Project of Microsoft; Grant of JR EAST Company and Hitachi Company, Japan. He served as Associate Editor, Guest Editor, Program Chair, Area Chair, Program Committee Member or reviewer for many famous journals and top-tier conferences, such as IEEE Transactions on Multimedia, WWW Journal, Big Data Journal, ACM TIST, IEEE TKDE, UbiComp, ICCV, CVPR, ICRA and etc. His main research interest are AI and its related research areas, such as data mining, intelligent system, computer vision, and robotics, especially on intelligent surveillance and information system design, mobility and spatio-temporal data mining, sensor fusion, and machine learning algorithms development. By now, he has published more than 50 technical publications in journals, book chapter, and international conference proceedings, including more than 30 high-impact papers in top-tier publications for computer science and robotics, such as ACM TOIS, ACM TIST, IEEE TPAMI, IEEE Intelligent System, KDD, UbiComp, IJCAI, AAAI, ICCV, CVPR, ECCV, ICRA and etc. His research was featured in many Japanese and international media, including United Nations, the Discovery Channel, and Fast Company Magazine. He received Honorable Mention Award in UbiComp 2015. Dr. Ryosuke Shibasaki Professor, Center for Spatial Information Science The University of Tokyo, Japan Dr. Shibasaki's research interest covers mobile big data analysis for development, satellite/aerial imagery and sensor data analysis including automated mapping with deep learning, human behavior understanding and modeling and data assimilation of discrete moving objects. He has published around 280 academic journal papers and 900 proceeding papers. He is also promoting use of personal information under the control of individuals. "Information bank" is a concept proposed by him (https://www.tedxtokyo.com/tedxtokyo_talk/information-bank/) . He is a former President of Asian GIS Association, a former President of the GIS Association of Japan. He also served as a board member of Infrastructure Implementation Board of GEO (Group of Earth Observations), a steering committee member of World Data System of ICSU (International Council for Science). Now, He works as a committee member on space policy of Cabinet Office of Japan, with the expertise of geospatial information technology and application development leveraged by space technology. Dr. Ryosuke Shibasaki obtained a Ph.D. in remote sensing/GIS from the University of Tokyo in 1986. His working experiences include Research engineer at Public Works Research Institute, Ministry of Construction, Japan (1982-1988), Associate Professor at the Department of Civil Engineering (1988-1991) and the Institute of Industrial Science, the University of Tokyo (1991-98), Professor at the Center for Spatial Information Science (1998-present. Director 2005-2010), the University of Tokyo.

1. Big Data and MaaS 2. MaaS system Development and APPs 3. Spatio-temporal Data Pre-processing Technologies 4. Travel Similarity Estimation and Clustering 5. Data Fusion Technologies for MaaS 6. Data-driven Optimization Technologies for MaaS 7. Data-driven Estimation for Urban Travel Shareability 8. MaaS system Data mining Technologies 9. IoT Technologies for MaaS 10. MaaS System Visualization 11. MaaS for Urban Sustainable Development

Erscheinungsdatum
Verlagsort Philadelphia
Sprache englisch
Maße 152 x 229 mm
Gewicht 450 g
Themenwelt Sachbuch/Ratgeber Natur / Technik Fahrzeuge / Flugzeuge / Schiffe
ISBN-10 0-323-90169-7 / 0323901697
ISBN-13 978-0-323-90169-7 / 9780323901697
Zustand Neuware
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