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.
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.
1. Overview of Data-driven Transportation Science2. Data-driven Energy Efficient Driving Control in Connected Vehicle Environment3. Machine Learning and Computer Vision-Enabled Traffic Sensing Data Analysis and Quality Enhancement4. Data Driven Approaches for Estimating Travel Time Reliability5. Urban Travel Behavior Study Based on Data Fusion Model6. Urban Travel Mobility Exploring with Large-Scale Trajectory Data7. Public Transportation Big Data Mining and Analysis8. Data Driven Gating Control for Network Based on Macroscopic Fundamental Diagram9. Simulation-Based Optimization for Network Modeling with Heterogeneous Data10. Network Modeling and Resilience Analysis of Air Transportation: A Data-Driven, Open-Source Approach
Yinhai Wang - Ph.D., P.E., Professor, Transportation Engineering,
University of Washington, USA. Dr. Yinhai Wang is a fellow of both
the IEEE and American Society of Civil Engineers (ASCE). He also
serves as director for Pacific Northwest Transportation Consortium
(PacTrans), USDOT University Transportation Center for Federal
Region 10, and the Northwestern Tribal Technical Assistance Program
(NW TTAP) Center. He earned his Ph.D. in transportation engineering
from the University of Tokyo (1998) and a Master in Computer
Science from the UW (2002). Dr. Wang’s research interests include
traffic sensing, transportation data science, artificial
intelligence methods and applications, edge computing, traffic
operations and simulation, smart urban mobility, transportation
safety, among others. 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.
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