Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more.
Format:
CONTRIBUTORS: Yinhai Wang, Ziqiang Zeng
EAN: 9780128170267
COUNTRY: United States
PAGES:
WEIGHT: 480 g
HEIGHT: 229 cm
PUBLISHED BY: Elsevier Science Publishing Co Inc
DATE PUBLISHED: 2018-12-05
CITY:
GENRE: TRANSPORTATION / General
WIDTH: 152 cm
SPINE:
Book Themes:
Transport planning and policy, Transport technology and trades, Databases
Dr. Yinhai Wang is a professor in transportation engineering and the founding director of the Smart Transportation Applications and Research Laboratory (STAR Lab) at the University of Washington (UW). He also serves as director for Pacific Northwest Transportation Consortium (PacTrans), USDOT University Transportation Center for Federal Region 10. He earned his Ph.D. in transportation engineering from the University of Tokyo (1998. Dr. Wang’s active research fields include traffic sensing, transportation data science, artificial intelligence (AI) methods and applications, edge computing, traffic operations and simulation, smart urban mobility, transportation safety, etc. Dr. Wang is chair of the AI and Advanced Computing Committee of the Transportation Research Board (TRB) and co-chair for the Connected and Autonomous Vehicle Impact Committee for American Society of Civil Engineers (ASCE). He served as president of the ASCE Transportation and Development Institute (T&DI) in 2018-2019. Dr. Wang received the IEEE International Smart Cities Conference’s Best Paper Award for 2020 and ASCE Journal of Transportation Engineering Best Paper Award for 2003. He was also the winner of Institute of Transportation Engineers (ITE) Innovation in Education Award for 2018. Ziqiang Zeng is a Research Associate in Transportation Engineering at the University of Washington. He is the co-author of Fuzzy-Like Multiple Objective Multistage Decision Making (Springer, 2015) and author of peer-reviewed papers in journals such as IEEE Transactions on Fuzzy Systems, Computer-aided Civil and Infrastructure Engineering, Journal of Construction Engineering and Management-ASCE, Journal of Computing in Civil Engineering-ASCE, Applied Mathematical Modelling, Engineering Optimization. His research includes intelligent transportation systems, data-driven decision making, and transportation safety analysis.
