Fundamentals of Traffic Simulation (eBook)
XVIII, 442 Seiten
Springer New York (Verlag)
978-1-4419-6142-6 (ISBN)
The increasing power of computer technologies, the evolution of software en- neering and the advent of the intelligent transport systems has prompted traf c simulation to become one of the most used approaches for traf c analysis in s- port of the design and evaluation of traf c systems. The ability of traf c simulation to emulate the time variability of traf c phenomena makes it a unique tool for capturing the complexity of traf c systems. In recent years, traf c simulation - and namely microscopic traf c simulation - has moved from the academic to the professional world. A wide variety of traf- c simulation software is currently available on the market and it is utilized by thousands of users, consultants, researchers and public agencies. Microscopic traf c simulation based on the emulation of traf c ows from the dynamics of individual vehicles is becoming one the most attractive approaches. However, traf c simulation still lacks a uni ed treatment. Dozens of papers on theory and applications are published in scienti c journals every year. A search of simulation-related papers and workshops through the proceedings of the last annual TRB meetings would support this assertion, as would a review of the minutes from speci cally dedicated meetings such as the International Symposiums on Traf c Simulation (Yokohama, 2002; Lausanne, 2006; Brisbane, 2008) or the International Workshops on Traf c Modeling and Simulation (Tucson, 2001; Barcelona, 2003; Sedona, 2005; Graz 2008). Yet, the only comprehensive treatment of the subject to be found so far is in the user's manuals of various software products.
Preface 5
Contents 7
Contributors 8
About the Authors 10
1 Models, Traffic Models, Simulation, and Traffic Simulation 15
1.1 The Concept of Model: A Scientific Approach to Systems Analysis 15
1.2 The Model-Building Process: Methodological Framework 18
1.3 Algorithmic Framework for Dynamic Traffic Models: Dynamic Traffic Assignment and Dynamic User Equilibrium 24
1.4 Principles of Traffic Flow Modeling 29
1.4.1 Macroscopic Modeling of Traffic Flows 29
1.4.2 Microscopic Modeling of Traffic Flows 32
1.4.3 Mesoscopic Modeling of Traffic Flows 47
1.5 Calibration and Validation of Traffic Simulation Models 51
1.6 Concluding Remarks 70
References 71
2 Microscopic Traffic Flow Simulator VISSIM 77
2.1 History and Applications of VISSIM 77
2.2 Model Building Principles 80
2.2.1 System Architecture 80
2.2.2 Infrastructure Modeling 82
2.2.2.1 Links and Connectors 82
2.2.2.2 Other Network Elements 83
2.2.3 Traffic Modeling 84
2.2.3.1 Private Transport 84
2.2.3.2 Public Transport 85
2.2.4 Traffic Control 85
2.2.4.1 Non-signalized Intersections 85
2.2.4.2 Signalized Intersections 86
2.2.5 Data Output 87
2.3 Fundamental Core Models 88
2.3.1 Car Following 88
2.3.2 Lateral Movements 90
2.3.2.1 Lane Selection 91
2.3.2.2 Lane Changing 92
2.3.2.3 Continuous Lateral Movement 93
2.3.3 Tactical Driving Behavior 94
2.3.3.1 Anticipated Driving at Conflict Areas 94
2.3.3.2 Cooperative Merging 95
2.3.4 Pedestrian Modeling 95
2.3.4.1 Path Choice 96
2.3.5 Fixed Routes 96
2.3.6 Dynamic Routing 97
2.3.7 Dynamic Assignment 98
2.4 Calibration and Validation 100
2.4.1 Calibration Based on Microscopic Data 102
2.5 Interfaces to External Applications 102
2.5.1 Application Programming Interface 102
2.5.2 External Signal Controllers 104
2.5.3 External Driver Model 105
2.5.4 External Emission Modeling 105
References 106
3 Traffic Simulation with AVENUE 108
3.1 Introduction 108
3.2 Modeling Principles 109
3.2.1 Hybrid Approach of the Traffic Flow Modeling 109
3.2.2 Dynamic Route Choice Model 109
3.2.3 Common Framework of Network Traffic Simulation Models 110
3.2.4 All-In-One Software Package 110
3.3 Traffic Flow Modeling 111
3.3.1 The Hybrid Block Density Method 111
3.3.2 Modeling of Lane Choice and Traffic Regulations 113
3.4 Dynamic Traffic Assignment 114
3.4.1 Modeling Principal for the Dynamic Route Choice Behavior 114
3.4.2 Dual-Graph Expression for the Route Guidance 115
3.5 Calibration and Validation 116
3.5.1 Promotion of Verification and Validation Policy by JSTE 116
3.5.1.1 Necessity of Standardized Verification and Validation 116
3.5.1.2 Japanese Verification Manual and Benchmark Data Set for Validation 117
3.5.1.3 Verification of AVENUE with Standard Verification Manual 118
3.5.1.4 Vehicle Generation 119
3.5.1.5 Shockwave Propagation 119
3.5.1.6 DSUO in the Route Choice Model 120
3.5.2 Validation of AVENUE with Standard Benchmark Data Set 122
3.6 Extended Modeling Capabilities: Working with External Applications 125
3.6.1 Time-Dependent OD Estimation 125
3.6.1.1 Outline of the Method 125
3.6.1.2 Relationship Between OD Flow and Link Flow 125
3.6.1.3 Estimation of Unique OD Matrix 126
3.6.1.4 Application to Tokyo Metropolitan Expressway 126
3.6.2 Automatic Parameter Tuning 127
3.6.2.1 Introduction 127
3.6.2.2 Tuning Parameters to Reproduce Link Travel Time 128
3.6.2.3 Tuning Parameters to Reproduce Link Traffic Volume 129
3.6.2.4 Range of Capacity Value and Step Size of Updating 130
3.6.3 Valuation Platform of Vehicle Probe Information System 130
3.6.4 Valuation Platform for Adaptive Signal Control System 132
3.7 Selected Overview of Advanced Case Studies and Applications Estimation of City-Scale Noise Level Distribution from Road Traffic 135
3.8 Modeling Details of Advanced Case Studies 138
3.8.1 Capacity Reduction by a On-Street-Parked Vehicle 138
3.8.2 LRT and Public Transportation Priority System 138
3.8.3 Pedestrian Crossing 139
References 141
4 Traffic Simulation with Paramics 143
4.1 Introduction 143
4.2 Applications 144
4.3 Model-Building Principles 144
4.3.1 Principles 144
4.3.2 Network Construction 144
4.3.2.1 Road Network 145
4.3.2.2 Signals 146
4.3.2.3 Zoning Scheme 146
4.3.3 Vehicles and Demand 147
4.3.3.1 Vehicles 147
4.3.3.2 Demand 147
4.3.3.3 Profiles 148
4.3.3.4 Passenger Transport 149
4.3.3.5 Time Periods 149
4.3.4 Presentation 149
4.4 Simulation Model 150
4.4.1 Environment 150
4.4.1.1 Trajectories and Geometry 150
4.4.1.2 Hazards 152
4.4.1.3 Vehicle Behaviour 153
4.4.1.4 Lane Choice and Lane Change 154
4.4.1.5 Speed and Acceleration 155
4.5 Gap Acceptance 156
4.6 Assignment 157
4.6.1 Driver Knowledge 158
4.6.2 Road Network 158
4.6.2.1 Car Parks 159
4.6.3 Static Assignment 159
4.6.4 Dynamic Assignment 160
4.6.4.1 Multiple-Level Routeing 161
4.7 Calibration and Validation 162
4.7.1 Assignment Calibration 162
4.7.1.1 Demand 162
4.7.1.2 Assignment 163
4.7.2 Behaviour Calibration 163
4.7.2.1 Network-Wide Vehicle Behaviour 164
4.7.2.2 Link and Junction Vehicle Behaviour 164
4.7.3 Validation 166
4.7.3.1 Flows 166
4.7.3.2 Journey Times and Queue Lengths 166
4.8 Extensions 167
4.8.1 Data Processing 167
4.8.1.1 Flows 167
4.8.1.2 Queues 168
4.8.1.3 Journey Times 169
4.8.1.4 Events 170
4.8.2 Batch Farm 170
4.8.3 PEARS 171
4.8.4 Signal Control and ITS 171
4.8.4.1 Advanced Control Interface (ACI) 171
4.8.4.2 ACI Example: Automated Traffic Management 173
4.8.4.3 ACI Example: UTC Signal Control 174
4.8.4.4 ACI Example: Signal Controller 175
4.9 Case Studies 175
4.9.1 Large Models 175
4.9.1.1 Plymouth 175
4.9.1.2 Chelmsford 176
4.9.1.3 Alkmaar 176
4.9.2 UTC and ITS 178
4.9.2.1 Hampton Court Flower Show 178
4.9.2.2 M25 179
4.9.2.3 Car Park Guidance 180
4.9.3 Road Design Studies 181
4.9.3.1 Overtaking Study 181
5 Traffic Simulation with Aimsun 184
5.1 Introduction 184
5.1.1 Background and Overview 184
5.1.2 Development Principles 185
5.2 Model-Building Principles in Aimsun 187
5.2.1 Model Building 187
5.2.1.1 Supply Data 188
5.2.1.2 Geometric and Functional Specification of the Road Network 188
5.2.1.3 Traffic Control 189
5.2.1.4 Public Transport 190
5.2.1.5 Demand Data 190
5.2.2 Model Verification, Calibration and Validation 191
5.2.3 Output Analysis 192
5.3 Fundamental Core Models: Car Following and Lane Changing 193
5.3.1 Microscopic Logic of Simulation Process 194
5.3.2 Mesoscopic Logic of simulation Process 194
5.3.3 Modelling Microscopic Vehicle Movement 194
5.3.3.1 Microscopic Car Following 195
5.3.3.2 Microscopic Lane-Changing Model 197
5.3.3.3 Microscopic Look-Ahead Model 199
5.3.3.4 Microscopic Gap-Acceptance Model 200
5.3.4 Modelling Mesoscopic Vehicle Movement 201
5.3.4.1 Mesoscopic Car following 201
5.3.4.2 Mesoscopic Lane Selection Model 202
5.3.4.3 Mesoscopic Gap-Acceptance Model 202
5.4 Dynamic Traffic Assignment 203
5.4.1 Dynamic Traffic Assignment Based on Discrete Choice Theory (Stochastic Route Choice) 205
5.4.2 Dynamic Traffic Assignment via an Iterative Heuristic (Stochastic Route Choice with Memory/Additional Information) 208
5.4.3 Dynamic Traffic Assignment via the Method of Successive Averages (Dynamic User Equilibrium) 209
5.4.4 Methodology and Data Flows for Dynamic Traffic Assignment 211
5.4.4.1 OD Matrix Data Flows 212
5.4.4.2 Path Assignment Data Flows 212
5.5 Calibration and Validation of Aimsun models 214
5.5.1 General Remarks 214
5.5.2 Verification and Validation in Aimsun 216
5.5.2.1 Verification 216
5.5.3 Validation 218
5.5.3.1 Comparison Based on Global Measurements 219
5.5.3.2 Comparison Based on Time Series Analysis 220
5.5.3.3 Comparison Based on Band Analysis 222
5.5.4 Calibration 222
5.5.4.1 Calibration of Behavioural Models 224
5.5.4.2 Calibration of Dynamic Traffic Assignment 227
5.6 Extended Modelling Capabilities: Working with External Applications 228
5.6.1 SDK Aimsun Platform 229
5.6.2 Micro API 230
5.6.3 Micro/Mesomodel SDK 231
5.7 Selected Overview of Advanced Case Studies and Applications 231
5.7.1 The Paris Tramway 231
5.7.2 Behaviour-Based Highway Performance Assessment 233
5.7.3 The Zaragoza Tramway 235
5.8 Modelling Details of Advanced Case Studies 237
5.8.1 The Aimsun Online Application in Madrid 238
5.8.2 Challenges and Further Needs 239
References 241
6 Traffic Simulation with MITSIMLab 244
6.1 Introduction 244
6.2 Model-Building Principles in MITSIMLab 246
6.3 Fundamental Core Models 247
6.3.1 Driving Behavior 247
6.3.2 Travel Behavior 250
6.3.3 Traffic Control 252
6.3.4 Transit Representation 254
6.3.5 Measures of Performance 254
6.4 Dynamic Traffic Assignment 255
6.5 Calibration and Validation 256
6.5.1 Overall Framework 256
6.5.2 Disaggregate Model Estimation 257
6.5.3 Aggregate Calibration 260
6.5.4 Validation 264
6.6 Extended Modeling Capabilities: Working with External Applications 265
6.6.1 Closed Loop with DynaMIT 267
6.6.2 Hybrid Simulation 268
6.7 Advanced Case Studies and Applications 270
6.7.1 ATIS Evaluation and Design 270
6.7.2 Evaluation of Advanced Signal Priority Strategies 272
6.8 Advanced Modeling Details 274
6.8.1 Freeway Merging Model 275
6.8.2 Arterial Lane-Changing Model 276
References 278
7 Traffic Simulation with SUMO Simulation of Urban Mobility 280
7.1 Introduction 280
7.2 Model Building Principles in SUMO 284
7.2.1 Preparing a Road Network to Simulate 284
7.2.2 Preparing the Demand 286
7.2.3 Summary on Preparing a Simulation Scenario 288
7.3 Fundamental Core Models 288
7.3.1 Longitudinal Vehicle Movement 288
7.3.2 Lane-Changing Model 291
7.3.3 Summary on Used Models 293
7.4 Dynamic Traffic Assignment 293
7.5 Calibration and Validation 296
7.6 Extended Modeling Capabilities: Working with External Applications 297
7.7 Selected Projects, Contribution, and Data 298
7.7.1 DELPHI 298
7.7.2 iTETRIS 299
7.7.3 SUMO Traffic Modeler 300
7.7.4 TAPAS Cologne 301
References 302
8 Traffic Simulation with DRACULA 305
8.1 Introduction 305
8.2 Model Building Principles in DRACULA 305
8.3 DRACULA Model Structure 306
8.3.1 Data Source and Linkage with Conventional Network Models 308
8.4 The Traffic Simulation 308
8.4.1 Simulation Time Periods and Simulation Loop 309
8.4.2 Car-Following Model 310
8.4.3 Car-Following on Motorway Links 311
8.4.4 Lane-Changing Model 313
8.4.5 Look-Ahead Factors in Lane Changing 313
8.4.6 On-Ramp Merge 314
8.5 Dynamic Traffic Assignment 316
8.5.1 The Full, Day-To-Day DTA Model in DRACULA 317
8.5.2 A Simple DTA Model 318
8.6 Model Calibration and Validation 319
8.6.1 Calibrating Car-Following Models on Open Highway 319
8.6.2 Calibration and Validation of the Motorway Merge Model 321
8.6.3 Calibration for the Distribution of Travel Time 323
8.7 Extended Modelling Capabilities and Advanced Applications 325
8.7.1 Overtaking on Two-Lane Rural Roads 325
8.7.2 Integrated Highway and Public Transport Network Model 326
8.7.3 Summary 331
References 331
9 Traffic Simulation with Dynameq 333
9.1 Model Building Principles 333
9.1.1 Introduction 333
9.1.2 Model Building Principles: Dynamic Traffic Assignment 335
9.1.3 Modeling Building Principles: Traffic Flow Simulation 337
9.2 Core Traffic Flow Models 339
9.3 Dynamic Traffic Assignment 342
9.3.1 Mathematical Model 342
9.3.2 MSA-Based Algorithm 343
9.3.3 Gradient-Like Algorithm 345
9.3.4 Time-varying Step Size Adjustment 347
9.4 Calibration and Advanced Modeling Features 348
9.4.1 Calibration and Stability 350
9.4.2 Calibration: Overview 353
9.4.2.1 Qualitative Analysis 353
9.4.2.2 Quantitative Analysis 353
9.4.3 Traffic Flow Calibration Calibration 354
9.4.3.1 Advanced Modeling Features 360
9.4.4 Route Choice Calibration 360
9.4.4.1 Advanced Modeling Features 361
9.4.5 Calibration -- Future Directions 362
9.5 Selected Applications 362
References 370
10 Traffic Simulation with DynaMIT 372
10.1 Introduction 372
10.2 Model Building Principles in DynaMIT 373
10.3 Fundamental Core Models 376
10.3.1 Demand 376
10.3.2 Supply 378
10.3.3 Online Estimation and Calibration of Parameters and Inputs 380
10.3.4 Graphical User Interface (GUI) 383
10.4 Dynamic Traffic Assignment 384
10.5 Calibration and Validation 385
10.5.1 Off-Line Calibration 387
10.5.2 Validation 389
10.6 Extended Modeling Capabilities: Working with External Applications 395
10.6.1 Interface with Other TMC Applications 395
10.6.2 Closed Loop 396
10.6.3 Innovative User Interfaces Through Mash-ups/Web Services 396
10.7 Selected Overview of Advanced Case Studies and Applications 396
10.7.1 Irvine: Predictive VMS 396
10.7.2 Lower Westchester County, NY -- Incident Diversion Strategies 399
10.7.3 Other Applications 401
10.8 Modeling Details of Advanced Case Studies 403
References 405
11 Traffic Simulation with METANET 408
11.1 Introduction 408
11.2 Model Building Principles in METANET 410
11.3 Core Traffic Flow Models 413
11.3.1 Links 414
11.3.2 Nodes 417
11.3.3 Model Summary 418
11.3.4 User-Programmable Control Strategies 419
11.4 Dynamic Traffic Assignment 419
11.5 Calibration and Validation 422
11.6 Extended Modeling Capabilities 426
11.6.1 Online Metanet 426
11.6.2 Metanet-DTA 427
11.6.3 AMOC 428
11.6.4 RENAISSANCE 429
11.7 Selected Overview of Advanced Case Studies and Applications 430
11.7.1 Automatic Control of VMS in the Interurban Scottish Highway Network 430
11.7.2 Ramp Metering Pilot Project for the Monash Freeway 431
11.7.3 Optimal Control Results for the Amsterdam Ring Road 434
References 438
Subject Index 440
| Erscheint lt. Verlag | 6.1.2011 |
|---|---|
| Reihe/Serie | International Series in Operations Research & Management Science |
| Zusatzinfo | XVIII, 442 p. |
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Naturwissenschaften ► Geowissenschaften ► Geografie / Kartografie | |
| Sozialwissenschaften ► Politik / Verwaltung | |
| Sozialwissenschaften ► Soziologie ► Spezielle Soziologien | |
| Technik ► Bauwesen | |
| Technik ► Elektrotechnik / Energietechnik | |
| Wirtschaft ► Allgemeines / Lexika | |
| Wirtschaft ► Betriebswirtschaft / Management ► Planung / Organisation | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| Wirtschaft ► Volkswirtschaftslehre ► Wirtschaftspolitik | |
| Schlagworte | Landscape/Regional and Urban Planning • linear optimization • Mathematical Programming • Operations Research • Simulation • Traffic Simulation • Transport • Transportation Engineering • Transportation Modeling |
| ISBN-10 | 1-4419-6142-9 / 1441961429 |
| ISBN-13 | 978-1-4419-6142-6 / 9781441961426 |
| Haben Sie eine Frage zum Produkt? |
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