Hamdar Traffic and Granular Flow '17

1. Auflage 2019
ISBN: 978-3-030-11440-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 526 Seiten

This book presents 57 peer-reviewed papers from the 12th Conference on Traffic and Granular Flow (TGF) held in Washington, DC, in July 2017. It offers a unique synthesis of the latest scientific findings made by researchers from different countries, institutions and disciplines.
The research fields covered range from physics, computer science and engineering and they may be all grouped under the topic of 'Traffic and Granular Flow'. The main theme of the Conference was: 'From Molecular Interactions to Internet of Things and Smart Cities: The Role of Technology in the Understanding and the Evolution of Particle Dynamics'.

Autoren/Hrsg.

Hamdar, Samer H.

Inhaltsverzeichnis

1;Preface;5
2;International Scientific Committee;7
3;Contents;9
4;Contributors;14
5;Part I Roadway Vehicular Flow: Data Collection, Modeling and Simulation;22
5.1;Exact Formula of Time-Headway Distribution for TASEP with Random-Sequential Update;23
5.1.1;1 Introduction;23
5.1.2;2 Model Definition;24
5.1.3;3 Time-Headway Distribution;25
5.1.4;4 Calculation for Random-Sequential Update;26
5.1.5;5 Conclusions;29
5.1.6;Appendix;29
5.1.7;References;30
5.2;Impact of Next-Nearest Leading Vehicles on Followers' Driving Behaviours in Mixed Traffic;31
5.2.1;1 Introduction;31
5.2.2;2 Experiment;32
5.2.3;3 Driving and Vehicle Characteristics;33
5.2.4;4 Impact of Next-Nearest Leaders' Presence;34
5.2.5;5 Effective Factors of Next-Nearest Leaders;35
5.2.6;6 Conclusion;37
5.2.7;References;38
5.3;Higher-Order Continuum Model and Its Numerical Solutions for Heterogeneous Traffic Flow with Non-lane Discipline;39
5.3.1;1 Introduction;39
5.3.2;2 Development of New Dynamic Model;40
5.3.3;3 Mathematical Properties;42
5.3.4;4 Numerical Simulation;43
5.3.4.1;4.1 Shock and Rarefaction Waves;44
5.3.4.2;4.2 Perturbation;44
5.3.5;5 Conclusions;47
5.3.6;References;47
5.4;Static Traffic Assignment on Ensembles of Synthetic Road Networks;48
5.4.1;1 Introduction;48
5.4.2;2 Review of Static Traffic Assignment;49
5.4.3;3 Network Synthesis Model;50
5.4.4;4 Results;53
5.4.5;5 Conclusions;54
5.4.6;References;55
5.5;The Effect of Traffic Signals on the Macroscopic Fundamental Diagram;56
5.5.1;1 Introduction;56
5.5.2;2 Methodology;58
5.5.2.1;2.1 Simulation Set-Up;58
5.5.2.2;2.2 Car-Following Parameters;58
5.5.2.3;2.3 Traffic Lights and Turn Fractions;59
5.5.2.4;2.4 Analyses;59
5.5.3;3 Results;60
5.5.4;4 Discussion;61
5.5.5;5 Conclusions;62
5.5.6;References;63
5.6;Braess Paradox in Networks of Stochastic Microscopic Traffic Models;64
5.6.1;1 Introduction;64
5.6.2;2 The Model;65
5.6.2.1;2.1 The Totally Asymmetric Exclusion Process;65
5.6.2.2;2.2 The Braess Network;66
5.6.2.3;2.3 Possible States in Our Networks;68
5.6.3;3 Results;69
5.6.4;4 Summary;70
5.6.5;References;71
5.7;Dynamical Universality Class of the Nagel–Schreckenberg and Related Models;72
5.7.1;1 Introduction;72
5.7.2;2 Dynamical Universality Classes;73
5.7.3;3 Nagel–Schreckenberg Model;76
5.7.3.1;3.1 Generalizations;77
5.7.4;4 Relaxation of Models;77
5.7.5;5 Summary and Conclusions;78
5.7.6;References;79
5.8;Prediction of Moving Bottleneck and Associated Traffic Phenomena for Automated Driving;80
5.8.1;1 Moving Bottleneck Scenario;80
5.8.2;2 Kerner–Klenov Simulation Model;81
5.8.3;3 Identification of Phase Transition Points;81
5.8.4;4 Reconstruction of the Moving Bottleneck Velocity;83
5.8.5;5 Statistical Analysis for Different Penetration Rates of Probe Vehicles;84
5.8.6;6 Results and Discussion;85
5.8.7;7 Outlook;86
5.8.8;References;87
5.9;F?S?F Transitions in Vehicle Probe Data;89
5.9.1;1 Introduction;89
5.9.1.1;1.1 Motivation to Study Speed Disturbances Before Traffic Breakdown;90
5.9.1.2;1.2 Background: Kerner's F?S Instability and F?S?F Transitions at Highway Bottlenecks;91
5.9.1.3;1.3 The Objective and Outline of This Paper;91
5.9.2;2 Floating Car Data and the Problem of Fine Spatiotemporal Analysis;92
5.9.2.1;2.1 Features of Empirical Single Vehicle Data Used in the Paper;92
5.9.3;3 Microscopic Empirical Features of F?S?F Transitions Before Traffic Breakdown;93
5.9.3.1;3.1 Benefits for ITS Applications;94
5.9.4;4 Conclusions;95
5.9.5;References;95
5.10;Microscopic Jam Tail Warning for Automated Driving;97
5.10.1;1 Introduction;97
5.10.1.1;1.1 Motivation;98
5.10.1.2;1.2 Outline of This Paper;98
5.10.1.3;1.3 Three-Phase Traffic Theory;99
5.10.1.3.1;1.3.1 Free Flow;99
5.10.1.3.2;1.3.2 Synchronized Flow;99
5.10.1.3.3;1.3.3 Wide Moving Jam;100
5.10.2;2 Identifying Traffic States from Empirical Microscopic Data;100
5.10.3;3 Microscopic State Transitions;101
5.10.3.1;3.1 Evaluation;101
5.10.4;4 Conclusions and Outlook;103
5.10.5;References;103
5.11;Study of Vehicle-Following Behavior Under Heterogeneous Traffic Conditions;105
5.11.1;1 Background;105
5.11.2;2 Study Sections;107
5.11.3;3 Identification of Following Pairs;108
5.11.4;4 Conclusions;112
5.11.5;5 Summary;112
5.11.6;References;113
5.12;Development of a Decision-Making Model for Merging Maneuvers: A Game Theoretical Approach;114
5.12.1;1 Introduction;114
5.12.2;2 Development of a Decision-Making Lane-Changing Model for Merging;115
5.12.2.1;2.1 Game Design;115
5.12.2.2;2.2 Payoff Functions in the Merging Decision-Making Game;116
5.12.2.2.1;2.2.1 Simplified Payoff Functions for the DS;117
5.12.2.2.2;2.2.2 Simplified Payoff Functions for the DL;117
5.12.3;3 Model Evaluation;118
5.12.3.1;3.1 Data Preparation;118
5.12.3.2;3.2 Classification of Driver's Actions;118
5.12.3.3;3.3 Model Evaluation;118
5.12.3.3.1;3.3.1 Model Calibration;118
5.12.3.3.2;3.3.2 Model Validation;119
5.12.4;4 Conclusions;122
5.12.5;References;122
5.13;Macroscopic Fundamental Diagram Validation for Collision Formation on Freeway Networks;124
5.13.1;1 Introduction;124
5.13.2;2 Background;125
5.13.3;3 Data;127
5.13.4;4 MFD Estimation and PCA Formulation;128
5.13.5;5 Results;129
5.13.6;6 Conclusions;132
5.13.7;References;133
5.14;Towards a More Stable Traffic Flow Performance: Applying and Calibrating the Intelligent Driver Model;134
5.14.1;1 Introduction;135
5.14.2;2 Literature Review;135
5.14.3;3 Methodology;136
5.14.4;4 Results;138
5.14.5;5 Conclusions;140
5.14.6;References;141
5.15;Numerical Comparison Between Traffic Flow Models with and Without Adaptation Behavior;142
5.15.1;1 Introduction;142
5.15.2;2 The Macroscopic Traffic Models;143
5.15.3;3 Comparison of the Models;144
5.15.4;4 Numerical Results and Concluding Remarks;145
5.15.5;References;147
5.16;A Game-Theoretic Approach for Minimizing Delays in Autonomous Intersections;148
5.16.1;1 Introduction;148
5.16.2;2 Definitions;149
5.16.3;3 Overview of Our Traffic Model;150
5.16.4;4 Correlated Equilibria in Independent Set Games;151
5.16.5;5 Independent Sets and Non-crossing Matchings;152
5.16.6;6 Experimental Results;154
5.16.7;7 Conclusion;155
5.16.8;References;156
6;Part II Pedestrian Traffic: Analytical and Empirical Studies;157
6.1;Empirical Evaluation of Crowds Using Automated Methods;158
6.1.1;1 Introduction;158
6.1.2;2 Empirical Evaluation of the Hajj;159
6.1.2.1;2.1 Velocity Extraction;159
6.1.2.2;2.2 Density Extraction;160
6.1.2.3;2.3 Perspective Correction;162
6.1.3;3 Temporal Study of the Hajj;162
6.1.4;4 Conclusions;164
6.1.5;References;165
6.2;Micro and Macro Pedestrian Dynamics in Counterflow: The Impact of Social Groups;166
6.2.1;1 Introduction;166
6.2.2;2 Description of Experiments;167
6.2.3;3 Data Analysis;168
6.2.3.1;3.1 Microscopic Analysis on Dyads;169
6.2.3.2;3.2 Effects of Dyads at a Macroscopic Scale;172
6.2.4;4 Conclusions and Future Works;173
6.2.5;References;173
6.3;Pedestrian Flow Through Complex Infrastructure, Experiments, and Mass-Transport Processes;174
6.3.1;1 Introduction;174
6.3.2;2 Experiments;176
6.3.3;3 Estimation Method;177
6.3.4;4 Observations;179
6.3.5;5 Summary and Conclusions;180
6.3.6;References;181
6.4;Mining the Social Media Data for a Bottom-Up Evaluation of Walkability;182
6.4.1;1 Introduction;182
6.4.2;2 Enabling Data;184
6.4.3;3 City Entities Identification;185
6.4.4;4 City Entities Characterization;186
6.4.5;5 Conclusions and Future Works;188
6.4.6;References;189
6.5;Experimental Investigation of Pedestrian Queuing Behaviour;191
6.5.1;1 Introduction;191
6.5.2;2 Experiment;193
6.5.3;3 Analysis Results;194
6.5.3.1;3.1 Bottleneck Capacity;194
6.5.3.2;3.2 Lateral Distribution of Pedestrian Queue;195
6.5.3.3;3.3 Spatial Distribution of Walking Speed;197
6.5.4;4 Conclusion;198
6.5.5;References;199
6.6;Safety Training Through Educational Online Computer Games on Crowd Evacuations?;200
6.6.1;1 Introduction;200
6.6.2;2 Methods;201
6.6.3;3 Results;204
6.6.4;4 Discussion and Outlook;206
6.6.5;References;207
6.7;Hybrid Tracking System for Pedestrians in Dense Crowds;208
6.7.1;1 Introduction;208
6.7.2;2 Hybrid Tracking System;210
6.7.2.1;2.1 IMU Tracking;210
6.7.2.2;2.2 Fusion of Camera and IMU Data;211
6.7.2.2.1;2.2.1 Merging the Datasets;211
6.7.2.2.2;2.2.2 Constraining the Drift;211
6.7.3;3 Results;213
6.7.4;4 Conclusion and Outlook;215
6.7.5;References;215
6.8;Investigating the Effect of Social Groups in Uni-directional Pedestrian Flow;217
6.8.1;1 Introduction;217
6.8.2;2 A Model for Group Cohesion;218
6.8.3;3 Calibration of the Group Behaviour;220
6.8.4;4 Validation at Basic Movement;221
6.8.5;5 Analysis of Group Influence on a Bottleneck Scenario;222
6.8.6;6 Conclusions;224
6.8.7;References;224
6.9;Towards Microscopic Calibration of Pedestrian Simulation Models Using Open Trajectory Datasets: The Case Study of the Edinburgh Informatics Forum;226
6.9.1;1 Introduction;227
6.9.2;2 Related Works;227
6.9.3;3 Methodology;228
6.9.4;4 Case Study;230
6.9.4.1;4.1 Selected Trajectories and Final Dataset;230
6.9.4.2;4.2 Model Specification and Calibration;231
6.9.4.3;4.3 Results;232
6.9.5;5 Discussion and Conclusion;233
6.9.6;References;233
6.10;Influence of Gender on the Fundamental Diagram and Gait Characteristics;235
6.10.1;1 Introduction;236
6.10.2;2 Experiments and Methods;237
6.10.3;3 Results and Discussions;238
6.10.4;4 Conclusions;241
6.10.5;References;243
6.11;Evaluation of Pedestrian Density Distribution with Respect to the Velocity Response;245
6.11.1;1 Introduction;245
6.11.2;2 Definitions;246
6.11.3;3 Analysis;247
6.11.4;4 Conclusions;251
6.11.5;References;252
6.12;Using Raspberry Pi for Measuring Pedestrian Visiting Patterns via WiFi-Signals in Uncontrolled Field Studies;254
6.12.1;1 Introduction;254
6.12.2;2 Related Work;255
6.12.3;3 Methodology;256
6.12.3.1;3.1 Device Assembly;257
6.12.3.2;3.2 Use in Uncontrolled Field Studies;258
6.12.3.3;3.3 Post-processing;259
6.12.3.4;3.4 Strategic Model Validation;259
6.12.4;4 Accuracy Discussion;260
6.12.5;5 Conclusion;261
6.12.6;References;261
6.13;Group Parameters for Social Groups in Evacuation Scenarios;263
6.13.1;1 Introduction;263
6.13.2;2 The Group Parameters;264
6.13.2.1;2.1 Centre of Mass;264
6.13.2.2;2.2 Shape of a Social Group;265
6.13.2.3;2.3 Orientation of a Social Group;265
6.13.3;3 Empirical Study;266
6.13.3.1;3.1 Mean Velocity of the Centre of Mass;266
6.13.3.2;3.2 Mean Aspect Ratio of the Minimal Area Ellipse;266
6.13.3.3;3.3 Mean Normalized Area of the Minimal Area Ellipse;268
6.13.3.4;3.4 Orientation;268
6.13.4;4 Conclusion;270
6.13.5;References;270
6.14;Simulating Assisted Evacuation Using Unity3D;272
6.14.1;1 Introduction;273
6.14.2;2 Background;273
6.14.2.1;2.1 Evacuation Models;274
6.14.2.2;2.2 Assisted Evacuation Modelling;275
6.14.3;3 Methodology;275
6.14.4;4 Explicit Assisted Evacuation Sub-model;276
6.14.4.1;4.1 Agents Type;276
6.14.4.2;4.2 Transportation Devices;277
6.14.4.3;4.3 Assisted Evacuation Algorithms;277
6.14.5;5 Case Study;279
6.14.6;6 Results;280
6.14.7;7 Conclusions and Outlooks;280
6.14.8;References;281
6.15;An Application of New Pedestrian Tracking Sensors for Evaluating Platform Safety Risks at Swiss and Dutch Train Stations;283
6.15.1;1 Introduction;284
6.15.2;2 Path Data Quality Definition;285
6.15.3;3 Experiment Setup;286
6.15.4;4 Results;288
6.15.5;5 Conclusions;290
6.15.6;References;292
6.16;Influence of Pedestrian Density on the Use of the Danger Zone at Platforms of Train Stations;293
6.16.1;1 Introduction;294
6.16.1.1;1.1 Background and Importance;294
6.16.1.2;1.2 Assumptions;295
6.16.1.3;1.3 Hypotheses;295
6.16.2;2 Methodology;296
6.16.2.1;2.1 Description of Datasets;296
6.16.2.2;2.2 Description of Data Analysis;296
6.16.3;3 Results;297
6.16.3.1;3.1 Sensor Data;297
6.16.3.2;3.2 Survey;300
6.16.4;4 Conclusion;300
6.16.5;5 Discussion;300
6.16.5.1;5.1 Practical Use for Station Planning;300
6.16.5.2;5.2 Further Research;301
6.16.6;References;301
6.17;Detecting Competitive Behaviors in Conflicts;303
6.17.1;1 Introduction;303
6.17.2;2 Cellular-Automata Floor-Field Model with Normal and Aggressive Agents;304
6.17.2.1;2.1 Static Floor Field and Update Rule;304
6.17.2.2;2.2 Conflict;305
6.17.2.3;2.3 Normal Agents and Aggressive Agents;306
6.17.3;3 Total Egress Time and Egress Time Ratio;307
6.17.4;4 Method for Detecting Aggressive Agents;308
6.17.5;5 Evolution of Accuracy of the Detecting Method;309
6.17.6;6 Summary;310
6.17.7;References;310
6.18;Towards Faster Navigation Algorithms on Floor Fields;312
6.18.1;1 Introduction;312
6.18.2;2 An Efficient Implementation of DistMesh;313
6.18.3;3 The Fast Marching Method on Unstructured Meshes;316
6.18.4;4 Performance;317
6.18.5;5 Conclusion and Future Work;318
6.18.6;References;320
6.19;Automated Quality Assessment of Space-Continuous Models for Pedestrian Dynamics;321
6.19.1;1 Introduction;321
6.19.2;2 Verification and Validation Tests;322
6.19.3;3 Methodology;324
6.19.3.1;3.1 Comparison of Two Data-Clouds;324
6.19.3.2;3.2 Validity Factor;325
6.19.4;4 Results;326
6.19.5;5 Discussion and Conclusions;327
6.19.6;References;328
6.20;Prediction of Pedestrian Speed with Artificial Neural Networks;330
6.20.1;1 Introduction;330
6.20.2;2 Models;332
6.20.3;3 Data;333
6.20.4;4 Fitting the Neural Network;334
6.20.5;5 Model Comparison;335
6.20.6;6 Conclusion;336
6.20.7;References;337
6.21;Noise-Induced Stop-and-Go Dynamics;339
6.21.1;1 Introduction;339
6.21.2;2 Definition of the Stochastic Model;340
6.21.3;3 Numerical Experiments;342
6.21.4;4 Discussion;344
6.21.5;References;345
6.22;Evacuation Simulation and Experiment Without Exit Information;348
6.22.1;1 Introduction;348
6.22.2;2 Model;349
6.22.2.1;2.1 Basic Floor Field Model;349
6.22.2.2;2.2 Informed Evacuees (IEs) and Uninformed Evacuees (UEs);350
6.22.2.3;2.3 Judgment Mark;350
6.22.2.3.1;2.3.1 Varieties of J Dropped by Evacuees;350
6.22.2.3.2;2.3.2 Amount of J Dropped by Evacuees;350
6.22.2.3.3;2.3.3 Target Exit and the Inference of Blocked Exits;351
6.22.2.4;2.4 Extended Floor Field Model;352
6.22.2.4.1;2.4.1 kmS and kD of Informed Evacuees (IEs);352
6.22.2.4.2;2.4.2 kmS and kD of Uninformed Evacuees (UEs);352
6.22.3;3 Evacuation Simulation Using Extended Floor Field Model and Experiment to Validate the Model;352
6.22.4;4 Results;353
6.22.4.1;4.1 When Informed Evacuees (IEs) Are Uniformly Distributed;353
6.22.4.2;4.2 When Informed Evacuees (IEs) Are NOT Uniformly Distributed;353
6.22.5;5 Conclusion;355
6.22.6;References;355
6.23;Fluctuations in Pedestrian Evacuation Times: Going One Step Beyond the Exit Capacity Paradigm for Bottlenecks;357
6.23.1;1 Introduction;357
6.23.2;2 Beyond the Mean Exit Capacity: Fluctuations;358
6.23.2.1;2.1 Importance of Fluctuations;358
6.23.2.2;2.2 Origins of the Fluctuations;359
6.23.3;3 A Practical Method to Assess Statistical Fluctuations;360
6.23.3.1;3.1 Distribution of Time Gaps Between Successive Egresses;360
6.23.3.2;3.2 Micro-Macro Relation;360
6.23.3.3;3.3 Caveats and Possible Issues;361
6.23.4;4 Validity and Limits of the Micro-Macro Relation;361
6.23.4.1;4.1 Succinct Description of the Model;362
6.23.4.2;4.2 Validation of the Micro-Macro Relation;362
6.23.4.3;4.3 Limits to the Validity;363
6.23.4.4;4.4 Conclusion;363
6.23.5;References;364
6.24;Macroscopic Fundamental Diagram for Train Platforms;365
6.24.1;1 Introduction;365
6.24.2;2 Experimental Design;366
6.24.3;3 Methodology;367
6.24.4;4 Results;369
6.24.5;5 Conclusions;371
6.24.6;References;372
6.25;Towards Safer Pedestrian Traffic: Investigation of the Impact of Social Field Characteristic on Crowd Dynamics;373
6.25.1;1 Introduction;374
6.25.2;2 Methodology;374
6.25.2.1;2.1 The Basic Social Force Model;374
6.25.2.2;2.2 Tangential Force Module;376
6.25.2.3;2.3 Stopping Module;376
6.25.3;3 Results;377
6.25.4;4 Conclusion;379
6.25.5;References;380
6.26;Defining the Pedestrian Fundamental Diagram;382
6.26.1;1 Introduction;382
6.26.2;2 Existing Definition of the Pedestrian Fundamental Diagram;384
6.26.3;3 Aggregation Levels;384
6.26.3.1;3.1 Usage Based Classification;385
6.26.3.2;3.2 Model Based Classification;385
6.26.4;4 Defining of the Pedestrian Fundamental Diagram;386
6.26.5;5 Stochastic Variations in the Fundamental Diagram;387
6.26.6;6 Conclusions;388
6.26.7;References;389
7;Part III From Individual Interactions to Complex Systems: Airplanes, Bicycles, Mixed Flow, Particles and Traveler Behavior;391
7.1;Simulating Ground Traffic on Airports Using Cellular Automata: The CAMAT-Model;392
7.1.1;1 Introduction;392
7.1.1.1;1.1 Cellular Automata Models (CA);393
7.1.1.2;1.2 Duesseldorf Airport (EDDL);393
7.1.2;2 The CAMAT-Model;393
7.1.3;3 Calibrating the CAMAT-Model;395
7.1.3.1;3.1 Concept of Collecting Data of Duesseldorf Airport;396
7.1.3.2;3.2 Comparison between Real-World Data and Simulation;396
7.1.4;4 Summary and Outlook;398
7.1.5;References;400
7.2;Investigating Passengers' Seating Behavior in Suburban Trains;401
7.2.1;1 Introduction;401
7.2.2;2 Field Observation;402
7.2.3;3 The Seating Model: Algorithm and Test;405
7.2.4;4 Conclusion;407
7.2.5;References;407
7.3;How Long Does It Take to Board an Airplane?;410
7.3.1;1 Introduction;411
7.3.2;2 Simulation Results and Analysis;412
7.3.3;3 A Test of Applicability and Final Conclusions;414
7.3.4;References;415
7.4;Assessment of Pedestrian Fatality Risk at Unsignalized Crosswalks by Means of Simulation;417
7.4.1;1 Introduction;418
7.4.2;2 Simulation Model;418
7.4.2.1;2.1 General Architecture and Motion Rules;418
7.4.2.2;2.2 Estimation of Collision Gravity;420
7.4.3;3 Results and Discussion;421
7.4.3.1;3.1 Driver's Attitude;421
7.4.3.2;3.2 Traffic Conditions;422
7.4.3.3;3.3 Distraction;423
7.4.4;4 Conclusions and Discussion;424
7.4.5;References;425
7.5;Algebraic and Geometric Aspects of Flow Modeling and Prospects of Natural Science Applications;426
7.5.1;1 Introduction: Complex Systems, Big Data, Supercomputers, and Mathematical Models;427
7.5.2;2 Complex Systems, Geometry of Supporter, and Movement Rules;427
7.5.3;3 Algebra of Complex Systems Models;428
7.5.4;4 Regular Networks and Qualitative Behavior of Local Flows;429
7.5.5;5 Rational Multipendulums;431
7.5.6;6 Cellular, BML Traffic Model and Processes on the Ring;432
7.5.7;7 Mathematical Problems and Application Prospects;433
7.5.8;References;434
7.6;Crossing Behaviour of Social Groups: Insights from Observations at Non-signalised Intersection;436
7.6.1;1 Introduction;436
7.6.2;2 Data Collection;438
7.6.3;3 Results;439
7.6.3.1;3.1 Traffic Volumes;439
7.6.3.2;3.2 Level of Service;439
7.6.3.3;3.3 Speeds and Crossing Phases;440
7.6.3.4;3.4 Accepted Safety Gap;441
7.6.4;4 Final Remarks;442
7.6.5;References;443
7.7;Modeling and Solving of Multiple Conflict Situations in Shared Spaces;444
7.7.1;1 Introduction;444
7.7.2;2 Background;445
7.7.3;3 Modeling Approach;446
7.7.3.1;3.1 Aggregation of Probabilities;447
7.7.3.2;3.2 Choice and Application of a Strategy;448
7.7.4;4 Application;449
7.7.5;5 Conclusions;450
7.7.6;References;451
7.8;Vibration Driven Vehicles Flowing Through Bottlenecks;452
7.8.1;1 Introduction;452
7.8.2;2 Experimental Setup;453
7.8.3;3 Results;454
7.8.4;4 Conclusions;458
7.8.5;References;459
7.9;Conflict Model of Evacuees and Vehicles on Pedestrian Crossing in the Aftermath of Disaster;460
7.9.1;1 Introduction;460
7.9.2;2 Numerical Simulation Model;461
7.9.2.1;2.1 New Model;461
7.9.2.2;2.2 Simulation Cases;462
7.9.3;3 Results;464
7.9.3.1;3.1 Pedestrian Tracking;464
7.9.3.2;3.2 Simulation Results;466
7.9.4;4 Discussion;466
7.9.5;5 Conclusions;467
7.9.6;Appendix;467
7.9.7;References;469
7.10;Social Force Model Describing Pedestrian and Cyclist Behaviour in Shared Spaces;470
7.10.1;1 Introduction;470
7.10.2;2 Social Force Model;471
7.10.3;3 Model Face-Validation Methodology;474
7.10.4;4 Face-Validation Results;476
7.10.5;5 Conclusions and Outlook;478
7.10.6;References;479
7.11;Multi-Attribute, Multi-Class, Trip-Based, Multi-Modal Traffic Network Equilibrium Model: Application to Large-Scale Network;480
7.11.1;1 Introduction;481
7.11.2;2 The Multi-Attribute, Multi-Class, Trip-Based, Multi-Modal Model;482
7.11.2.1;2.1 STA equilibrium model;483
7.11.2.2;2.2 DTA equilibrium model;484
7.11.3;3 Trip-Based Solution Algorithm and Numerical Experiments;485
7.11.4;4 Numerical Results and Conclusion;487
7.11.5;References;488
7.12;Microscopic Cycling Behavior Model Using Differential Game Theory;489
7.12.1;1 Introduction;489
7.12.2;2 Microscopic Modeling Approaches;490
7.12.3;3 Cycling Behavior Model Derivation;491
7.12.4;4 Model Plausibility Demonstration;493
7.12.4.1;4.1 Model Interpretation;493
7.12.4.2;4.2 Face Validation;495
7.12.5;5 Conclusions;497
7.12.6;References;497
7.13;Simulating Bicycle Traffic by the Intelligent-Driver Model: Reproducing the Traffic-Wave Characteristics Observed in a Bicycle-Following Experiment;499
7.13.1;1 Introduction;499
7.13.2;2 Models Under Investigation;500
7.13.3;3 Ring-Road Experiment;501
7.13.4;4 Methods;501
7.13.4.1;4.1 Calibrating and Validating Trajectories;501
7.13.4.2;4.2 Comparing Microscopic Fundamental Diagrams;502
7.13.5;5 Results;502
7.13.5.1;5.1 Free Acceleration;502
7.13.5.2;5.2 Collective Driving Behavior;503
7.13.5.3;5.3 Microscopic Fundamental Diagrams Comparison;504
7.13.5.4;5.4 Stop-and-Go Waves;504
7.13.5.5;5.5 Inter-Driver Variation and Validation;505
7.13.6;6 Discussion and Conclusions;506
7.13.7;References;506
7.14;Large-Scale Modeling of VANET and Transportation Systems;508
7.14.1;1 Introduction;509
7.14.2;2 VANET Communication and the Medium Access Technique;510
7.14.2.1;2.1 The Proposed Medium Access Model Versus Previous Models;510
7.14.2.2;2.2 Model Derivation;510
7.14.2.3;2.3 Communication Model Validation;512
7.14.3;3 Transportation Traffic Modeling;512
7.14.3.1;3.1 Eco-Routing Application;513
7.14.3.2;3.2 Eco-Routing in Literature;513
7.14.4;4 Simulation and Results;514
7.14.5;5 Conclusions;516
7.14.6;References;516
7.15;Activity Location Recommendation Using a Decentralized Proportional Feedback Mechanism;518
7.15.1;1 Introduction;518
7.15.2;2 Model for the Uncontrolled System;519
7.15.3;3 Controller Design Problem Formulation;521
7.15.3.1;3.1 Feedback Control;522
7.15.4;4 Simulation of a 3-Node Network;524
7.15.5;5 Conclusions and Outlook;525
7.15.6;References;525