Big Data and Mobility as a Service
Coordonnateurs : Zhang Haoran, Song Xuan, Shibasaki Ryosuke
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
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
- Summarizes current fundamental MaaS technologies
- Shows how to utilize anonymous big data for transportation analysis and problem-solving
- Illustrates, with data-enabled shared transportation service examples from different countries, the similarities and differences within a global urban mobility framework
Date de parution : 10-2021
Ouvrage de 306 p.
15.2x22.8 cm