Data-Driven Solutions to Transportation Problems

Häftad, Engelska, 2018

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Beskrivning

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

Produktinformation

Utgivningsdatum:2018-12-05
Mått:152 x 229 x undefined mm
Vikt:480 g
Format:Häftad
Språk:Engelska
Antal sidor:299
Förlag:Elsevier Science
ISBN:9780128170267

Utforska kategorier

Databaser inom Data och IT
Regional planering inom Naturvetenskap och teknik
Transportteknik inom Naturvetenskap och teknik

Mer om författaren

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 ComputerScience 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.

Innehållsförteckning

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