The Evolution of Travel Time Information Systems: The Role of Comprehensive Traffic Models and Improvements Towards Cooperative Driving Environments

Preface
Overall Approach and Outline
Contents
Part I Introduction to Travel Time Information
1 Traffic Monitoring and Reconstruction
	1.1 Introduction
	1.2 Eulerian Sensing Versus Lagrangian Sensing
		1.2.1 Eulerian Sensors in Traffic Monitoring
		1.2.2 Lagrangian Sensors in Traffic Monitoring
	1.3 Traffic Reconstruction
	References
2 Travel Time Information Revisited
	2.1 Travel Time Information and Traffic Management
		2.1.1 Value of Travel Time Information
	2.2 Travel Time Definitions and Estimation Methods
		2.2.1 Direct Travel Time Measurements
		2.2.2 Indirect Travel Time Estimation
		2.2.3 Data Fusion for Travel Time Estimation
	2.3 Dissemination of Travel Time Information
	References
Part II New Travel Time Estimation Methods
3 A Simple Algorithm for the Estimation of Road Traffic Space Mean Speeds from Data Available to Most Management Centers
	3.1 Introduction
	3.2 Background
	3.3 Simple Algorithm for the Estimation of Space Mean Speeds from the Data Provided by Double-Loop Detectors
	3.4 Implementation of the Algorithm with Artificial Data
	3.5 Implementation of the Algorithm with Real Data
		3.5.1 The Data
		3.5.2 The Results
		3.5.3 Comparison Between the Proposed Algorithm and Other Methods
		3.5.4 Discussion
	3.6 In Search of Other Relationships Between Mean Speeds
	3.7 Conclusions and Further Research
	References
4 Accurate, Affordable and Widely Applicable Freeway Travel Time Prediction: Fusing Vehicle Counts with Data Provided by New Monitoring Technologies
	4.1 Introduction and Background
	4.2 Travel Time from Input–Output Cumulative Curves
		4.2.1 Travel Time Definitions from Input–Output Diagrams
		4.2.2 Main Difficulties When Using Input–Output Diagrams for Travel Time Estimation
	4.3 A Data Fusion Algorithm for the Short-Term Prediction of Freeway Travel Times
		4.3.1 Data Inputs for the Algorithm
		4.3.2 The Short-Term Travel Time Prediction
		4.3.3 The Data Fusion Method to Correct Detector Drift
		4.3.4 Simpler Process if the Direct Travel Time Measurements Are ITT
		4.3.5 The Algorithm Turn-On and Turn-Off Conditions
	4.4 Implementation of the Algorithm with Real AVI Data on the AP7 Freeway in Spain
		4.4.1 Layout, Available Data and Considered Parameters
		4.4.2 Obtained Results and Discussion
	4.5 Implementation of the Algorithm with Simulated ITT Data
		4.5.1 Layout, Available Data and Considered Parameters
		4.5.2 Obtained Results and Discussion
	4.6 Conclusions and Further Research
	References
5 Travel Time Information Systems in the Era of Cooperative Automated Vehicles
	5.1 Introduction
	5.2 Cooperative Automated Driving Structure and Technological Aspects
		5.2.1 The Vehicles
		5.2.2 Communications
		5.2.3 The Cloud
		5.2.4 The Infrastructure
		5.2.5 Other Agents
	5.3 Impact of Cooperative Automated Vehicles on Mobility
		5.3.1 New Approaches for an Improved Traffic Performance
		5.3.2 Expected Impact on Mobility Trends and Figures
		5.3.3 Contribution to Safer Mobility
	5.4 The Role and the Evolution of Travel Time Information Systems in Cooperative Driving Environments
	5.5 Conclusions
	References
Part III Data Analytics and Models for Dynamic Traffic Management
6 Dynamic Traffic Management: A Bird’s Eye View
	6.1 Introductory Remarks
	6.2 ITS Approaches and Artificial Intelligence
	6.3 Current Hybrid Approaches
	6.4 Other Approaches
	6.5 AMS Approach and ATDM
	6.6 Concluding Remarks
	References
7 Data Analytics and Models for Understanding and Predicting Travel Patterns in Urban Scenarios
	7.1 Dynamic Traffic Assignment Models
		7.1.1 Determining the Path-Dependent Flow Rates by MSA: Convergence Criterion to Equilibrium
		7.1.2 Dynamic Network Loading
	7.2 The Static Formulation of the OD-Estimation Problem
	7.3 Bi-level Optimization Models for OD Adjustment
	7.4 Analytical Formulations for the Dynamic OD Matrix Estimation (DODME) Problem
	7.5 Practical Applications for Traffic Management
		7.5.1 Analytical Approaches Based on State-Space Modeling
		7.5.2 Aimsun Live
		7.5.3 Simulation-Based Approaches: Stochastic Perturbation Stochastic Approximation (SPSA)
	7.6 Data-Driven Approaches
		7.6.1 A Conceptual Proposal on Data-Driven Modeling
		7.6.2 Accounting for Mobility Learning from ICT Data Collection
		7.6.3 Estimating Assignment Matrices from FCD Data
	7.7 Measuring the Quality of the OD Estimates
	7.8 Concluding Remarks
	References
Part IV Overall Conclusions and Further Research
8 Overall Conclusions and Further Research
	8.1 Overall Conclusions
	8.2 Further Research
Glossary