Barceló / Kuwahara Traffic Data Collection and its Standardization

1. Auflage 2010
ISBN: 978-1-4419-6070-2
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 243 Seiten

A nice night of October 2007, in Beijing, during the XV World Conference on ITS a number of colleagues met informally for a dinner party that spontaneously became a vivid discussion on the importance of traffic data for all types of p- poses. Researchers can hardly do any progress in modeling, developing, and te- ing theories without suitable data, and what practitioners can do in real life is limited not only by technology but also by the availability of the required data. Quite frequently, the data and not the technologies are what determine how far we can go. Any discussion about traffic data leads in a natural way to a discussion on the variety of traffic data sources, formats, levels of aggregation, accuracies, and so on. Consequently, we moved to talk on the initiative that Kuwahara had undertaken in his traffic laboratory at the University of Tokyo, known as the International Traffic Data Base, and thus smoothly but inexorably we came to agree that it would be convenient to organize a workshop to continue our discussion at a more formal level, share our points of view with other colleagues, listen what they had to say and, if possible, d- seminate the findings in our professional and academic communities.

Autoren/Hrsg.

Barceló, Jaume

Kuwahara, Masao

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Chapter 1: Traffic Data Collection and Its Standardization;14
3.1;1.1 Introduction;14
3.2;1.2 Data Collection;15
3.2.1;1.2.1 Data Sources and Measurement Stations;15
3.2.2;1.2.2 Data Storage, Provision, and Needs;17
3.3;1.3 Data Usage;18
3.4;1.4 Data Standards;20
3.5;1.5 Data Availability;20
3.5.1;1.5.1 Data Storage and Usage;21
3.5.2;1.5.2 Privacy Concerns;21
3.5.3;1.5.3 Provision Costs;22
3.5.4;1.5.4 Other Factors;22
3.6;1.6 Final Thoughts;22
4;Chapter 2: Data Collection, Use and Provision at the Transport Data Centre, New South Wales, Australia;24
4.1;2.1 Introduction;24
4.2;2.2 TDC Datasets and Methodology;25
4.2.1;2.2.1 Household Travel Survey (HTS);25
4.2.2;2.2.2 Journey to Work (JTW);27
4.2.3;2.2.3 Commercial Transport Study (CTS);29
4.2.3.1;2.2.3.1 Overview of the Estimation Procedure;29
4.2.3.2;2.2.3.2 CTS Validation;30
4.2.3.3;2.2.3.3 Scope of the CTS;31
4.2.3.4;2.2.3.4 Recent Developments – The New Freight Movement Model;31
4.2.4;2.2.4 Travel Zone Population and Employment Forecasts;31
4.2.5;2.2.5 Strategic Travel Model (STM);33
4.3;2.3 TDC Use of Traffic Count Data;35
4.4;2.4 Issues Related to Data Access and Use;35
4.5;2.5 Summary;36
4.6;References;37
5;Chapter 3: Data Collection for Measuring Performance of Integrated Transportation Systems*;38
5.1;3.1 Introduction;38
5.2;3.2 Data Needs for Multimodal Transportation Systems;39
5.2.1;3.2.1 Data for Measuring Freeway Performance;39
5.2.2;3.2.2 Data for Measuring Performance of Arterial Highways;40
5.2.3;3.2.3 Data for Measuring Performance of Transit Operations;43
5.2.4;3.2.4 Data for Measuring Performance of Integrated Corridor Management;43
5.3;3.3 California PATH Parsons Traffic and Transit Laboratory;44
5.3.1;3.3.1 Data Collection;45
5.3.2;3.3.2 Data Management;46
5.3.3;3.3.3 Experimental Environment;48
5.4;3.4 Role of Parsons T2 Lab in Supporting PATH Research;49
5.4.1;3.4.1 Evaluation of TSP and Development of New TSP Approaches;49
5.4.2;3.4.2 Developing Optimized Control for Urban Railway Crossings;52
5.4.3;3.4.3 Development Red-Light-Running Collision Avoidance System;53
5.5;3.5 Concluding Remarks;57
5.6;References;58
6;Chapter 4: International Traffic Database: Gathering Traffic Data Fast and Intuitive;59
6.1;4.1 Introduction;59
6.2;4.2 Contributions to Traffic Engineering;61
6.3;4.3 Overall System Design;62
6.4;4.4 Metadata Search Engine;63
6.5;4.5 Data Storage;64
6.5.1;4.5.1 Data Stored in ITDb;64
6.5.2;4.5.2 Data Stored in Third Party Locations;65
6.6;4.6 Universal Data Translator;65
6.7;4.7 User Front End;66
6.8;4.8 Services and Tools;67
6.9;4.9 Future Developments;67
6.10;References;68
7;Chapter 5: Data Mining for Traffic Flow Analysis: Visualization Approach;69
7.1;5.1 Introduction;69
7.2;5.2 Characteristics of Traffic Detector Data;70
7.3;5.3 Process for Visualizing Traffic Detector Data;71
7.3.1;5.3.1 Color Mapping System for TCM;72
7.3.2;5.3.2 Time-and-Day TCM;73
7.3.3;5.3.3 Time-and-Value TCM;75
7.3.4;5.3.4 Implementation of the Visualization System;75
7.4;5.4 Empirical Analysis with Visualization;77
7.4.1;5.4.1 Analysis of Data Taken on the Hansin Expressway;77
7.4.1.1;5.4.1.1 Detection of Detector’s Error;77
7.4.1.2;5.4.1.2 Detection of Effect of Toll Gate Operation;78
7.4.1.3;5.4.1.3 Detection of Traffic Capacity Reduction after Sunset;78
7.4.2;5.4.2 Analysis of Data of Traffic on the Metropolitan Expressway;78
7.5;5.5 Conclusions;80
7.6;References;84
8;Chapter 6: The Influence of Spatial Factors on the Commuting Trip Distribution in the Netherlands;85
8.1;6.1 Introduction;85
8.2;6.2 Data;86
8.3;6.3 Method;87
8.4;6.4 Results;90
8.5;6.5 Conclusions;94
8.6;6.6 Appendix 1. Removal of False Reports;95
8.7;6.7 Appendix 2. Network Versus Reported Distances;96
8.8;6.8 Appendix 3. Internal Distances;97
8.9;References;98
9;Chapter 7: Dynamic Origin–Destination Matrix Estimation Using Probe Vehicle Data as A Priori Information;100
9.1;7.1 Introduction;100
9.2;7.2 Literature Review;101
9.2.1;7.2.1 PVD: Part of “Roads of the Future” Research Program;102
9.2.2;7.2.2 Deriving Road Networks from PVD;102
9.2.3;7.2.3 PVD for Traffic Monitoring;102
9.2.4;7.2.4 Local MAD Method for Probe Vehicle Data Processing;103
9.2.5;7.2.5 Real Time Route Analysis Based on PVD Technology;103
9.3;7.3 Methodology;103
9.3.1;7.3.1 Rules for Determining Origins and Destinations within the PVD;104
9.3.1.1;7.3.1.1 Rule 1: Real Stop Versus Intermediate Stop;104
9.3.1.2;7.3.1.2 Rule 2: Break Versus Lost Measurements;104
9.3.1.3;7.3.1.3 Rule 3: Vehicles Entering/Leaving Study Area;105
9.3.1.4;7.3.1.4 Rule 4: First and Last Measurements from a Vehicle;105
9.3.2;7.3.2 A Priori Matrix Estimation with PVD;106
9.3.3;7.3.3 Route Choice Analysis with PVD;106
9.3.4;7.3.4 Trip Length Distribution Analysis with PVD;108
9.3.4.1;7.3.4.1 TLD Obtained Directly from PVD;108
9.3.4.2;7.3.4.2 TLD Calculated from Estimated OD Matrices;108
9.4;7.4 Results;108
9.4.1;7.4.1 Sensitivity Analysis;108
9.4.1.1;7.4.1.1 Sensitivity of the Parameter Real Stop;109
9.4.1.2;7.4.1.2 Sensitivity of the Parameter Break;110
9.4.1.3;7.4.1.3 Conclusions from the Sensitivity Analysis;112
9.4.2;7.4.2 The Driving Behavior of Taxis;112
9.4.3;7.4.3 Estimations with the PVD;112
9.4.3.1;7.4.3.1 A Priori Matrix Estimation;113
9.4.3.2;7.4.3.2 Route Choice Analysis;115
9.4.4;7.4.4 Trip Length Distribution;115
9.4.5;7.4.5 Answers to Stated Questions;117
9.5;7.5 Conclusion and Further Work;118
9.6;References;118
10;Chapter 8: Using Probe Vehicle Data for Traffic State Estimation in Signalized Urban Networks;120
10.1;8.1 Introduction;120
10.2;8.2 The Delay Probability Distribution at Signalized Intersections;122
10.2.1;8.2.1 An Example with a Stochastic Initial Queue;128
10.3;8.3 Delay Monitoring from Probe Vehicles;129
10.4;8.4 Comparison with Simulation;133
10.5;8.5 The Estimation of the State of an Intersection;133
10.6;8.6 Some Initial Experience with the Fuzzy State Estimation;135
10.7;8.7 Conclusion and Discussion;137
10.8;References;138
11;Chapter 9: Floating Car Data Based Analysis of Urban Travel Times for the Provision of Traffic Quality;139
11.1;9.1 Introduction;139
11.2;9.2 Data Collection for Traffic Quality Determination;140
11.2.1;9.2.1 Traditional Approach;140
11.2.2;9.2.2 Data Collection by Telematics;141
11.3;9.3 Data Analysis for Traffic Quality Determination;143
11.3.1;9.3.1 Preprocessing;145
11.3.2;9.3.2 Data Mining;146
11.3.2.1;9.3.2.1 FCD Aggregation;147
11.3.2.2;9.3.2.2 FCD-Based Cluster Analysis;149
11.3.3;9.3.3 Verification;150
11.4;9.4 Example Application: Travel Times for the City of Stuttgart;151
11.4.1;9.4.1 Provision of Travel Times for Strategic Traffic Management;152
11.4.2;9.4.2 Provision of Travel Times for Planning in City Logistics;153
11.4.2.1;9.4.2.1 Evaluating Travel Times by Simulation;154
11.4.2.2;9.4.2.2 Computational Results;155
11.5;9.5 Conclusion;156
11.6;References;157
12;Chapter 10: A Cost-Effective Method for the Detection of Queue Lengths at Traffic Lights;160
12.1;10.1 Introduction;160
12.2;10.2 Description of the New Method;161
12.3;10.3 Results and Discussion;164
12.4;10.4 Conclusions;168
12.5;References;169
13;Chapter 11: Extended Floating Car Data in Co-operative Traffic Management;170
13.1;11.1 Introduction;171
13.2;11.2 Co-operative Traffic Management;171
13.3;11.3 Extended Floating Car Data;173
13.4;11.4 Advantages for Road Operators;174
13.5;11.5 xFCD Transmission Strategies;175
13.5.1;11.5.1 Attribute Categorization;175
13.5.2;11.5.2 Intelligent Communication Media Selection;176
13.5.3;11.5.3 Feedback Channel Referencing;177
13.6;11.6 Data Quality Aspects;177
13.7;11.7 Outlook;179
13.8;11.8 Conclusion;179
13.9;References;179
14;Chapter 12: Microscopic Data for Analyzing Driving Behavior at Traffic Signals;180
14.1;12.1 Introduction;180
14.2;12.2 Modeling Microscopic Driving Behavior at Signals;181
14.3;12.3 Data Collection and Processing;184
14.3.1;12.3.1 Data Requirements and Choice of the Study Area;184
14.3.2;12.3.2 Data Acquisition and Conversion Processes;186
14.3.3;12.3.3 Data Cleaning and Recording Individual Trajectories;188
14.3.4;12.3.4 Data Smoothing;189
14.4;12.4 Microscopic Analysis of Driving Behavior;194
14.5;12.5 Conclusions and Future Research;199
14.6;References;199