Lawrence A. Klein

ITS Sensors and Architectures for Traffic Management and Connected Vehicles (eBook, PDF)

• Format: PDF

Produktbeschreibung

Intelligent transportation systems, supported by data-gathering sensors and vehicle communications, increase safety, efficiency, predictability of traffic flow and potentially air quality. This book addresses the impact of automated and connected vehicles, systems engineering, and national ITS architectures in the US, Europe, Japan, and elsewhere.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 542
• Erscheinungstermin: 7. August 2017
• Englisch
• ISBN-13: 9781351800969
• Artikelnr.: 49041059

Autorenporträt

Lawrence A. Klein, has more than forty years experience of aerospace and traffic management experience, as a specialist in systems engineering and the development of sensors and data fusion concepts. He is a consultant and has been a research engineer, including working as Director of Advanced Technology Programs at Waveband Corporation and at the French National Institute for Transport and Safety Research. He is a Member of Transportation Research Board's Highway Traffic Monitoring Committee and past member of the Freeway Operations Committee, and led the ASTM E17 Group V-ITS development of worldwide standards to evaluate traffic sensors. Klein is also a Visiting Professor at Harbin Institute of Technology in China, and is the principal author of the third edition of the FHWA's Traffic Detector Handbook.

Inhaltsangabe

1 Introduction
2 Freeway traffic management centers
3 Sensor applications to ITS
4 Sensor data requirements
5 Modern traffic flow sensor technologies
6 Inductive loop installation and loop system sensitivity
7 Overhead sensor installation and initialization
8 Sensor field tests
9 Sensor specification and testing tools
10 Alternative sources of navigation and traffic flow data
11 Automated vehicles
12 Connected vehicles
13 Systems engineering process
14 National ITS architectures
15 Connected vehicle architectures and applications
16 Sensor and data fusion in traffic management
17 Bayesian inference and Dempster-Shafer evidential reasoning and their
application to traffic management