Advanced Mathematical Science for Mobility Society

Inbunden, Engelska, 2024

560 kr

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Beskrivning

This open access book presents the mathematical methods for huge data and network analysis.The automotive industry has made steady progress in technological innovations under the names of Connected Autonomous-Shared-Electric (CASE) and Mobility as a Service (MaaS). Needless to say, mathematics and informatics are important to support such innovations. As the concept of cars and movement itself is diversifying, they are indispensable for grasping the essence of the future mobility society and building the foundation for the next generation. Based on this idea, Research unit named "Advanced Mathematical Science for Mobility Society" was established at Kyoto University as a base for envisioning a future mobility society in collaboration with researchers led by Toyota Motor Corporation and Kyoto University.This book contains three main contents.1. Mathematical models of flow2. Mathematical methodsfor huge data and network analysis3. Algorithm for mobility societyThe first one discusses mathematical models of pedestrian and traffic flow, as they are important for preventing accidents and achieving efficient transportation. The authors mainly focus on global dynamics caused by the interaction of particles. The authors discuss many-body particle systems in terms of geometry and box-ball systems. The second one consists of four chapters and deals with mathematical technologies for handling huge data related to mobility from the viewpoints of machine learning, numerical analysis, and statistical physics, which also includes blockchain techniques. Finally, the authors discuss algorithmic issues on mobility society. By making use of car-sharing service as an example of mobility systems, the authors consider how to construct and analyze algorithms for mobility system from viewpoints of control, optimization, and AI.

Produktinformation

Utgivningsdatum:2024-03-14
Mått:155 x 235 x 18 mm
Vikt:506 g
Format:Inbunden
Språk:Engelska
Antal sidor:215
Förlag:Springer Verlag, Singapore
ISBN:9789819997718

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Mer om författaren

Kazushi Ikeda received his Ph.D. in Engineering from the University of Tokyo and is currently a professor at the Graduate School of Science and Technology at Nara Institute of Science and Technology. He specializes in research on mathematical informatics that includes machine learning theory and applications, mathematical biology, and computational neuroscience.Yoshiumi Kawamura received Ph.D. in Theoretical Chemistry at Waseda University in 2005. He joined Toyota Motor Corporation in 2005. He has been involved in research on simulation and informatics for the development of materials for automotive applications. His current position is a manager of the advanced research in Mathematical Sciences and Data Science, etc.Kazuhisa Makino received the Ph.D. in Applied Mathematics and Physics from Kyoto University. He is currently a professor of the Research Institute for Mathematical Sciences, Kyoto University. He has authored or coauthored morethan 200 peer-reviewed journal and conference papers. His research interests include discrete mathematics, optimization, and algorithm theory. He has received many scientific awards and honors, such as AAAI Outstanding Paper Award 2002, ISAAC Best Paper Award 2016, and Editors' Choice of Discrete Applied Mathematics 1999 and 2003.Satoshi Tsujimoto is a professor in Graduate School of Informatics at Kyoto University. He received his Ph.D. degree in engineering from Waseda University in 1997. He joined the faculty at Waseda University and moved to Osaka University and Kyoto University. His research interests are in applied mathematics, particularly with the theory of applications of discrete integrable systems, special functions, and orthogonal polynomials.Nobuo Yamashita received Ph.D. in Engineering at Nara Institute of Science and Technology in March 1996. After he was a research fellow at Japan Society for the Promotion of Science in 1996, he was appointed an assistant professor at the Section of Applied Mathematics and Physics, Graduate School of Engineering, Kyoto University in August 1997. In April 2005 he became an associate professor at the department of Applied Mathematics and Physics, Graduate School of Informatics, and In July 2014 he was promoted to professor. He is also the research director of the joint research project, "Advanced Mathematical Science for Mobility Society" between Kyoto University and Toyota Motor Corporation. His research interests include continuous optimization, equilibrium problems, and nonlinear equations.Shintaro Yoshizawa received a Ph.D. in Statistical Mathematics from the Graduate University for Advanced Studies in 1999. After being a visiting researcher at the Isaac Newton Institute for Mathematical Science in 2000, a postdoctoral researcher at the University of Würzburg from 2001 to 2002, and a visiting fellow at the Australian National University Institute of Advanced Studies in 2002, he joined Toyota Motor Corporation in 2003. He has been involved in R&D related to robotics intelligence, human characteristics, and mathematical science for the Mobility Society. His current position is as a project manager for advanced research in Robotics.mathematical science for Mobility Society. His current position is a project manager of the advanced research in Robotics.Hanna Sumita is an associate professor at the School of Computing, Tokyo Institute of Technology. She received Ph.D. in the field of Information Science and Technology from the University of Tokyo in 2015. Her research includes combinatorial optimization and algorithmic game theory.

Innehållsförteckning

Part 1. Introduction, motivation, and direction for Advanced Mathematical Science for Mobility Society, together with the project between Toyota Motor Corporation and Kyoto University.- Chapter 1. Advanced Mathematical Science for Mobility Society.- Part 2. Mathematical models of flow Chapter. 2. Analysis of many-body particle systems by geometry and box-ball-system theory.- Chapter 3. Discrete Integrable Systems, LR transformations and Box-Ball Systems.- Part 3. Mathematical methods for huge data and network analysis.- Chapter 4. Eigenvalue Analysis in Mobility Data.- Chapter 5. Application of tensor network formalism for processing tensor data.- Chapter 6. Machine Learning Approach to Mobility Analysis.- Chapter 7. Graph optimization problems and algorithms for DAG-type blockchains.- Part 4. Algorithm for mobility society.- Chapter 8. Control and optimization of one-way car-sharing systems.- Chapter 9. Algorithms for future mobility society Chapter 10. Mechanism Design for Mobility.