Junping Zhang | Akateeminen Kirjakauppa

The Series in Intelligent Systems publishes titles that cover state of the art knowledge and the latest advances in research and development in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. The Series publishes titles in three core sub-topic areas: Intelligent Automation, Intelligent Transportation Systems, and Intelligent Computing. Titles focus on professional and academic reference works and handbooks. The readership for the series is broad, reflecting the wide range of intelligent systems interest and application, but focuses on engineering (in particular automation, control, mechatronics, robotics, transportation, automotive, aerospace), electronics and electronic design, and computer science.

Intelligent Transportation Systems (ITS) are rapidly developing in the world and widely used to optimize the transportation management and plan, enhance city and country security, and improve the traffic users' travel route, safety and health directly or indirectly. To achieve these goals, a large amount of data are collected from a variety of sources related to ITS. Such abundant and various data lead to a revolution of changing ITS to a more powerful and multifunctional Data-driven ITS. As an associate editor in IEEE transactions on Intelligent Transportation Systems and an associate editor in IEEE Intelligent Systems, Junping Zhang surveys the development of Data-driven ITS in detail, discuss some potential issues closely related to ITS and give a roadmap on the future directions, as well as some recent works on the data-driven ITS. An important contribution in this book is that ITS is closely related to up-to-date data-driven techniques such as data mining, machine learning and computer vision, which are less involved in by other previous ITS-related books. For examples, privacy-preserving and people-centric ITS.

• The first book for the ITS community that focuses on how to utilize data collected from ITS. Data-driven Intelligent Transportation Systems is a rapidly developing topic. This book is the first dedicated to this important topic
• Introduces the latest approaches and techniques I ITYS, including dimension reduction technique such as nonlinear embedding that can address dimensionality, which often impair the effectiveness of ITS
• Includes a range of real word case studies that show how work in ITS, and related areas including machine learning, vision, and data mining can be applied to transportation systems