Crowd Dynamics, Volume 4: Analytics and Human Factors in Crowd Modeling (Modeling and Simulation in Science, Engineering and Technology)

Preface
Contents
Behavioral Human Crowds and Society
	1 Plan of the Chapter
	2 On the Contents of the Edited Book
	3 Considerations for Research Prospects
	References
The Mathematical Theory of Hughes\' Model: A Survey of Results
	1 Introduction
	2 Construction of the Model
		2.1 The Two-Dimensional Case
		2.2 The One-Dimensional Case
	3 A Riemann-Like Initial Datum
	4 Existence Results
	5 The Wave-Front Tracking Approach
	6 A Deterministic Particles Approach
	7 The Case of a Linear Cost Function
	8 Fixed-Point Existence Strategy
	9 Simulations
	10 Modified Versions
		10.1 The Regularised Hughes Model
		10.2 A Dynamic Version of Hughes Model via Optimal Control
		10.3 Optimal Control via Local Attraction
		10.4 A Localised Version of the Model
	11 Conclusions and Future Challenges
	References
Time-Continuous Microscopic Pedestrian Models: An Overview
	1 Introduction
	2 Force-Based Models
		2.1 Pioneer Force-Based Model by Hirai and Tarui
		2.2 Modern Force-Based Models
	3 Velocity-Based Models
		3.1 Velocity Obstacle Models
		3.2 General Collision-Free Mathematical Framework
	4 Anticipation in Pedestrian Models
		4.1 Different Degrees of Anticipation and Planning
		4.2 Anticipation Based on Velocity Obstacles
		4.3 Anticipation Based on Times to Collision
		4.4 Articulation Between the Cognitive Layer and the Mechanical Layer
		4.5 Differential Games
	5 Data-Based Calibration
		5.1 Data-Based Calibration of the Model Parameters
		5.2 Data-Based Calibration of Hybrid Models
	6 Data-Based Predictive Algorithms
		6.1 Long Short-Term Memory Networks
		6.2 Generative Adversarial Networks
	7 Modelling Development Perspectives
	References
Empirical Investigations on the Role of Psychological Factors in Pedestrian Route Choice
	1 Introduction
	2 Methods
		2.1 Virtual Experiments
		2.2 Stated Choice Experiments
		2.3 Modelling
		2.4 Statistical Models
	3 Example 1: Diminishing Sensitivity to Environmental Information
		3.1 Description of the Model
		3.2 Description of the Experiment
		3.3 Results
	4 Example 2: Route Commitment Effect
		4.1 Description of the Experiment
		4.2 Results
	5 Example 3: Responses to the Movement of Others
		5.1 Description of the Experiment
		5.2 Results
	6 Summary and Conclusions
	References
Social Human Collective Decision-Making and Its Applications with Brain Network Models
	1 Introduction
	2 DDMs and Bayesian Models for Decision-Making
		2.1 DDMs in Probabilistic Settings
		2.2 Bayesian Models for Decision-Making
			2.2.1 Input Process and Observational Sensory Information for Decision-Making
			2.2.2 Generative Models in Bayesian Cognitive Science
			2.2.3 Bayesian Inference for Decision-Making Processes
		2.3 Decision Policy for Decision-Making Processes
	3 Examples
		3.1 State-of-the-Art in Modelling Risky Decision-Making
		3.2 Numerical Results with DDM for a Decision-Making Model
		3.3 Bayesian Inference Modelling Spiking Neurons for Decision-Making Processes
		3.4 Numerical Results with the Bayesian Approach for a Decision-Making Model
	4 Collective Decision-Making and Brain Networks
	5 Examples of Collective Dynamics in the Approach Based on Brain Networks Considered as Collections of Neurons
	6 Remarks on Human Biosocial Dynamics with Complex Psychological Behaviour and Nonequilibrium Phenomena
	7 Conclusions
	References
Single-File Pedestrian Dynamics: A Review of Agent-Following Models
	1 Introduction
	2 Single-File Motion
		2.1 Stop-and-Go Waves
		2.2 Phase Separation
	3 Force-Based Models
		3.1 Exponential-Distance Models
		3.2 Algebraic-Distance Models
		3.3 Conceptual Problems
	4 Models for Single-File Motion
		4.1 Historical Overview
		4.2 Categorizing Following Models
	5 Generalized OV-Framework
		5.1 The Time-Gap and Optimal Velocity Models
		5.2 Reaction and Anticipation Time
		5.3 Summary of Model Relations
	6 Stability Analysis
		6.1 The Prolific 1950s and Early 1960s
		6.2 Resumption from the 1990s with Nonlinear Models
	7 The Effect of Noise
		7.1 White Noise Models
		7.2 Colored Noise Models
		7.3 Noise-Induced Stop-and-Go Waves
	8 Conclusion
	Appendix 1: Linear Stability Conditions for Models by Ordinary Differential Equations
		First Order Models
		Second Order Models
		Mixed Flow Models
		Interaction Model with K Predecessors
	Appendix 2: Linear Stability Conditions for Models by Delay Differential Equations
		First Order Models
		Second Order Models
	Appendix 3: Oscillations vs. Tunneling in the Social Force Model
	Appendix 4: Damped Harmonic Oscillator
	References
State-of-the-Art Passengers Survey Examining Passengers\' Crowd Behavior in Emergencies at Train Stations
	1 Introduction
		1.1 Objective and Scope
	2 Methodology
	3 Railway Stations
		3.1 Passengers\' Perceptions of Wayfinding Tools and Evacuation Procedures
		3.2 Passengers\' Likely Behavior
	4 Discussion
		4.1 Implications of the Evacuation Tools and Procedures
		4.2 Implications of Understanding Passengers\' Likely Behavior
		4.3 Limitations of the Survey Studies in Train Stations and Future Prospects
	5 Conclusion
	References
On a Kinetic Modeling of Crowd Dynamics with Several Interacting Groups
	1 Introduction
	2 Mathematical Description of the Kinetic Model
		2.1 Some Basic Concepts
		2.2 Representation of the System
		2.3 Modeling the Interactions
			2.3.1 Geometrical Effects
			2.3.2 Interactions Between Pedestrians
			2.3.3 The Interaction Term
	3 Numerical Tests
		3.1 Test 1: Evacuation of a Crowd with Two Groups Aiming for Different Exits
		3.2 Test 2: Separation (Clustering) of a Crowd with Three Groups Having Different Motility
		3.3 Test 3: Stripe Formation in the Intersection of Pedestrian Flows
		3.4 Test 4: Merging Flow at a T-junction
	4 Conclusion and Perspectives
	References
Coupling Pedestrian Flow and Disease Contagion Models
	1 Introduction
	2 Kinetic Evolution Equation
	3 The Infection Rate
		3.1 A Pure Collision Based Models
		3.2 Collision Based Model Taking into Account Contact Duration Time
		3.3 Dynamic Model Including Contact Duration
		3.4 Dynamic Model Using a Drift-Diffusion Equation
	4 The Hydrodynamic Model
		4.1 The Hydrodynamic Model Using Volume Fractions
	5 Numerical Method and Results
		5.1 Numerical Method
		5.2 Numerical Results
		5.3 Test-Case 1: Crowd at Rest
		5.4 Test-Case 2: Uni-directional Flow
		5.5 Test-Case 3: Bi-directional Flow
		5.6 Test-Case 4: Flow Around an Obstacle
		5.7 Test-Case 5: Flow Through a Bottleneck
		5.8 Comparison of the Number Exposed Pedestrian in Time
	6 Concluding Remarks
	References