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.

• 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