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Full Description
Written by an international team of researchers, this book focuses on traffic information processing and signal control using emerging types of traffic data. It conveys advanced methods to estimate and predict traffic flows at different levels, including macroscopic, mesoscopic and microscopic. The aim of these predictions is to optimize traffic signal control for intersections and to mitigate ever-growing traffic congestion.
The book begins with an introduction to the topic, its fundamental principles and recent developments. The first part of the book then covers the estimation and prediction of the traffic flow state based on emerging detailed data sources. Coverage in this section includes traffic analytics with online web data; macroscopic traffic performance indicators based on floating car data; short-term travel time prediction by deep learning a comparison of different LSTM-DNN models; short-term traffic prediction under disruptions using deep leaning; real time demand based traffic diversion; game theoretic lane change strategy for cooperative vehicles under perfect information; and cooperative driving and a lane change-free road transportation system.
The second part focuses on traffic signal control optimization, explaining how to use improved data and advanced tools for better signal control. Chapters include urban traffic control systems; algorithms and models for signal coordination; emerging technologies to enhance traffic signal coordination practices; control for short-distance intersections; and multi-day evaluation of adaptive traffic signal system based on license plate recognition detector data.
A valuable resource for researchers and engineers working in the field of traffic information and control, and intelligent transport systems, Traffic Information and Control offers an overview of recent research and practical approaches to optimising traffic signal control.
Contents
Chapter 1: Introduction
Part I: Modern traffic information technology
Chapter 2: Traffic analytics with online web data
Chapter 3: Macroscopic traffic performance indicators based on floating car data: formation, pattern analysis, and deduction
Chapter 4: Short-term travel-time prediction by deep learning: a comparison of different LSTM-DNN models
Chapter 5: Short-term traffic prediction under disruptions using deep learning
Chapter 6: Real-time demand-based traffic diversion
Chapter 7: Game theoretic lane change strategy for cooperative vehicles under perfect information
Chapter 8: Cooperative driving and a lane change-free road transportation system
Part II: Modern traffic signal control
Chapter 9: Urban traffic control systems: architecture, methods and development
Chapter 10: Algorithms and models for signal coordination
Chapter 11: Emerging technologies to enhance traffic signal coordination practices
Chapter 12: Traffic signal control for short-distance intersections with dynamic reversible lanes
Chapter 13: Multiday evaluation of adaptive traffic signal system based on license plate recognition detector data
Chapter 14: Conclusion
