Traffic Data Collection and its Standardization (International Series in Operations Research & Management Science, 144)

✍ Scribed by Jaume Barceló (editor), Masao Kuwahara (editor)

• Publisher: Springer
• Year: 2010
• Tongue: English
• Leaves: 202
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Chapter 1: Traffic Data Collection and Its Standardization
1.1 Introduction
1.2 Data Collection
1.2.1 Data Sources and Measurement Stations
1.2.2 Data Storage, Provision, and Needs
1.3 Data Usage
1.4 Data Standards
1.5 Data Availability
1.5.1 Data Storage and Usage
1.5.2 Privacy Concerns
1.5.3 Provision Costs
1.5.4 Other Factors
1.6 Final Thoughts
Chapter 2: Data Collection, Use and Provision at the Transport Data Centre, New South Wales, Australia
2.1 Introduction
2.2 TDC Datasets and Methodology
2.2.1 Household Travel Survey (HTS)
2.2.2 Journey to Work (JTW)
2.2.3 Commercial Transport Study (CTS)
2.2.3.1 Overview of the Estimation Procedure
2.2.3.2 CTS Validation
2.2.3.3 Scope of the CTS
2.2.3.4 Recent Developments – The New Freight Movement Model
2.2.4 Travel Zone Population and Employment Forecasts
2.2.5 Strategic Travel Model (STM)
2.3 TDC Use of Traffic Count Data
2.4 Issues Related to Data Access and Use
2.5 Summary
References
Chapter 3: Data Collection for Measuring Performance of Integrated Transportation Systems*
3.1 Introduction
3.2 Data Needs for Multimodal Transportation Systems
3.2.1 Data for Measuring Freeway Performance
3.2.2 Data for Measuring Performance of Arterial Highways
3.2.3 Data for Measuring Performance of Transit Operations
3.2.4 Data for Measuring Performance of Integrated Corridor Management
3.3 California PATH Parsons Traffic and Transit Laboratory
3.3.1 Data Collection
3.3.2 Data Management
3.3.3 Experimental Environment
3.4 Role of Parsons T2 Lab in Supporting PATH Research
3.4.1 Evaluation of TSP and Development of New TSP Approaches
3.4.2 Developing Optimized Control for Urban Railway Crossings
3.4.3 Development Red-Light-Running Collision Avoidance System
3.5 Concluding Remarks
References
Chapter 4: International Traffic Database: Gathering Traffic Data Fast and Intuitive
4.1 Introduction
4.2 Contributions to Traffic Engineering
4.3 Overall System Design
4.4 Metadata Search Engine
4.5 Data Storage
4.5.1 Data Stored in ITDb
4.5.2 Data Stored in Third Party Locations
4.6 Universal Data Translator
4.7 User Front End
4.8 Services and Tools
4.9 Future Developments
References
Chapter 5: Data Mining for Traffic Flow Analysis: Visualization Approach
5.1 Introduction
5.2 Characteristics of Traffic Detector Data
5.3 Process for Visualizing Traffic Detector Data
5.3.1 Color Mapping System for TCM
5.3.2 Time-and-Day TCM
5.3.3 Time-and-Value TCM
5.3.4 Implementation of the Visualization System
5.4 Empirical Analysis with Visualization
5.4.1 Analysis of Data Taken on the Hansin Expressway
5.4.1.1 Detection of Detector’s Error
5.4.1.2 Detection of Effect of Toll Gate Operation
5.4.1.3 Detection of Traffic Capacity Reduction after Sunset
5.4.2 Analysis of Data of Traffic on the Metropolitan Expressway
5.5 Conclusions
References
Chapter 6: The Influence of Spatial Factors on the Commuting Trip Distribution in the Netherlands
6.1 Introduction
6.2 Data
6.3 Method
6.4 Results
6.5 Conclusions
6.6 Appendix 1. Removal of False Reports
6.7 Appendix 2. Network Versus Reported Distances
6.8 Appendix 3. Internal Distances
References
Chapter 7: Dynamic Origin–Destination Matrix Estimation Using Probe Vehicle Data as A Priori Information
7.1 Introduction
7.2 Literature Review
7.2.1 PVD: Part of “Roads of the Future” Research Program
7.2.2 Deriving Road Networks from PVD
7.2.3 PVD for Traffic Monitoring
7.2.4 Local MAD Method for Probe Vehicle Data Processing
7.2.5 Real Time Route Analysis Based on PVD Technology
7.3 Methodology
7.3.1 Rules for Determining Origins and Destinations within the PVD
7.3.1.1 Rule 1: Real Stop Versus Intermediate Stop
7.3.1.2 Rule 2: Break Versus Lost Measurements
7.3.1.3 Rule 3: Vehicles Entering/Leaving Study Area
7.3.1.4 Rule 4: First and Last Measurements from a Vehicle
7.3.2 A Priori Matrix Estimation with PVD
7.3.3 Route Choice Analysis with PVD
7.3.4 Trip Length Distribution Analysis with PVD
7.3.4.1 TLD Obtained Directly from PVD
7.3.4.2 TLD Calculated from Estimated OD Matrices
7.4 Results
7.4.1 Sensitivity Analysis
7.4.1.1 Sensitivity of the Parameter Real Stop
7.4.1.2 Sensitivity of the Parameter Break
7.4.1.3 Conclusions from the Sensitivity Analysis
7.4.2 The Driving Behavior of Taxis
7.4.3 Estimations with the PVD
7.4.3.1 A Priori Matrix Estimation
7.4.3.2 Route Choice Analysis
7.4.4 Trip Length Distribution
7.4.5 Answers to Stated Questions
7.5 Conclusion and Further Work
References
Chapter 8: Using Probe Vehicle Data for Traffic State Estimation in Signalized Urban Networks
8.1 Introduction
8.2 The Delay Probability Distribution at Signalized Intersections
8.2.1 An Example with a Stochastic Initial Queue
8.3 Delay Monitoring from Probe Vehicles
8.4 Comparison with Simulation
8.5 The Estimation of the State of an Intersection
8.6 Some Initial Experience with the Fuzzy State Estimation
8.7 Conclusion and Discussion
References
Chapter 9: Floating Car Data Based Analysis of Urban Travel Times for the Provision of Traffic Quality
9.1 Introduction
9.2 Data Collection for Traffic Quality Determination
9.2.1 Traditional Approach
9.2.2 Data Collection by Telematics
9.3 Data Analysis for Traffic Quality Determination
9.3.1 Preprocessing
9.3.2 Data Mining
9.3.2.1 FCD Aggregation
9.3.2.2 FCD-Based Cluster Analysis
9.3.3 Verification
9.4 Example Application: Travel Times for the City of Stuttgart
9.4.1 Provision of Travel Times for Strategic Traffic Management
9.4.2 Provision of Travel Times for Planning in City Logistics
9.4.2.1 Evaluating Travel Times by Simulation
9.4.2.2 Computational Results
9.5 Conclusion
References
Chapter 10: A Cost-Effective Method for the Detection of Queue Lengths at Traffic Lights
10.1 Introduction
10.2 Description of the New Method
10.3 Results and Discussion
10.4 Conclusions
References
Chapter 11: Extended Floating Car Data in Co-operative Traffic Management
11.1 Introduction
11.2 Co-operative Traffic Management
11.3 Extended Floating Car Data
11.4 Advantages for Road Operators
11.5 xFCD Transmission Strategies
11.5.1 Attribute Categorization
11.5.2 Intelligent Communication Media Selection
11.5.3 Feedback Channel Referencing
11.6 Data Quality Aspects
11.7 Outlook
11.8 Conclusion
References
Chapter 12: Microscopic Data for Analyzing Driving Behavior at Traffic Signals
12.1 Introduction
12.2 Modeling Microscopic Driving Behavior at Signals
12.3 Data Collection and Processing
12.3.1 Data Requirements and Choice of the Study Area
12.3.2 Data Acquisition and Conversion Processes
12.3.3 Data Cleaning and Recording Individual Trajectories
12.3.4 Data Smoothing
12.4 Microscopic Analysis of Driving Behavior
12.5 Conclusions and Future Research
References