Overview
In the face of urbanization and growing transportation demands, ""Demand-Responsive Transit Systems: Operation and Management Solutions"" offers a comprehensive exploration of the innovative strategies and technologies shaping the future of public transit. Authored by industry experts Kai Liu and Tao Liu, this book is an essential resource for transit professionals, urban planners, and policy makers. Leverage the power of data and innovation to revolutionize demand-responsive transit (DRT) systems. This book delves into optimal routing design utilizing aggregated cell phone data and open-source GIS tools, alongside iterated clustering optimization for effective stop locating and bus routing. Discover how customized commuter bus routes can significantly reduce carbon emissions and how pricing strategies can be optimized by considering passenger loyalty decay. The book also explores the design of intelligent platoon transit systems aimed at transportation electrification, and modular bus unit scheduling under range and charging constraints. Further, it provides insights into the formation and scheduling optimization for electric modular buses with station-based demand responsiveness. Additionally, it highlights the coordinated operation of fixed-route and demand-responsive feeder transit services in travel corridors. These advanced strategies and practical applications are essential for enhancing operational efficiency and achieving sustainable urban mobility, making this book an indispensable resource for professionals in the field.
Full Product Details
Author: Kai Liu ,
Tao Liu ,
Jiangbo Wang
Publisher: Springer Verlag, Singapore
Imprint: Springer Verlag, Singapore
ISBN: 9789819564767
ISBN 10: 981956476
Pages: 218
Publication Date: 12 May 2026
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: Manufactured on demand
We will order this item for you from a manufactured on demand supplier.
Author Information
Kai Liu, Ph.D., Professor, professor at School of Economics and Management, Dalian University of Technology, China. His areas of specialties include AI supported transportation planning and management, travel behavior modeling, Geoscience in Transportation, Transportation Electrification and autonomous transport systems. He is an associate editor (AE) of Transportation Research Part D and Asian Transport Research. Dr. LIU Kai received his Ph.D. degree in Transportation Engineering from Nagoya University in 2006, and received a M. Eng. and a B.Eng. degree in Urban Planning from Tongji University, China. He has published 100+ academic papers in professional journals, among which 60+ papers have been indexed by SCI/SSCI. His papers have been non-self cited around 3400+ times according to Google Scholar. Recently, His studies focus on key technologies and methodologies in transportation field (including elaborate data collection and treatment, data fusion, pattern recognition, visualization and information mining), green transportation systems (including electric vehicles system and demand-responsive bus system, as well as the technologies for reducing traffic energy consumptions and emissions). Tao Liu, Ph.D., Professor, professor at Southwest Jiaotong University. He graduated from the University of Auckland, New Zealand. Dr. Liu's research interests include public transport, smart mobility, multimodal transport, and sustainable transport. He has published more than 80 papers in prestigious academic journals and conferences, including Transportation Research Part A/B/C/D/E, IEEE Transactions on ITS, Transport Policy, Transport Reviews, Omega, Computers & Industrial Engineering, and ISTTT. Jiangbo Wang, Ph.D., Associate Professor, graduated from Nagoya University in Japan with a Ph.D. degree, and then joined Dalian University of Technology, China until now. He serves as a reviewer for Transportation Research Part A/C/D, Transportation, Transport Policy and other mainstream journals. He has published more than 20 peer-reviewed papers in Transportation Research Part A/C/D/E, Transport Policy and other outstanding journals. His main research interests include travel behavior analysis, shared transportation system analysis, and transportation big data mining and modelling.
