Elsevier Big Data and Mobility as a Service

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. Review Shows how big data helps integrate modes of transport through mobility as a service (MaaS) From the Back Cover <p><i>Big Data and Mobility as a Service </i>explores MaaS platforms that can be adaptable to the ever-evolving mobility environment. It looks at multi-mode urban crowd data to assess the urban mobility characteristics, shared transportation potential and their performance conditions and constraints. It analyzes the roles of multimodality, travel behavior, urban mobility dynamics, and participation. Combining with insights on using big data to analyze the market and policy decisions, <i>Big Data and Mobility as a Service </i>is an essential tool for urban transportation management researchers and practitioners.</p> About the Author 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 procee