Data-Driven Solutions to Transportation Problems

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.

• Synthesizes the newest developments in data-driven transportation science
• Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed
• Useful for both theoretical and technically-oriented researchers

Yinhai Wang is a Professor of Transportation Engineering and Founding Director of the Smart Transportation Applications and Research Laboratory at the University of Washington, Director for Pacific Northwest Transportation Consortium, Director of the University Transportation Center for Federal Region 10, and Visiting Chair of Traffic Information and Control at Harbin Institute of Technology. He a steering committee member of the IEEE Smart Cities and President of the American Society of Civil Engineers Transportation and Development Institute. Dr. Wang's research include traffic sensing, e-science of transportation, big-data analytics, traffic operations and simulation, smart urban mobility, transportation safety, etc. He has written more than 120 peer reviewed journal articles and delivered more than 130 invited talks and 200 other academic presentations. Dr. Wang is Associate Editor of Journal of ITS, Journal of Computing in Civil Engineering, and Journal of Transportation Engineering.