01014804

Component

https://doi.org/10.3141/1917-17

http://scholar.google.com...fman&publication_year=2005

Loop detectors have been the preeminent detection technology for several decades, but they require closing of the right-of-way during installation and potentially undermine the integrity of the pavement surface if they are not installed before paving. As a result, there is great interest in emerging technologies that promise traffic detection without the liabilities of loop detectors, many of which have already been deployed in large numbers. The remote traffic microwave sensor (RTMS) is among the most widely deployed noninvasive traffic detectors. This study evaluates the performance of the RTMS in side-fire mode relative to loop detectors in freeway applications. First, the aggregated data reported by the RTMS using its internal controller emulation are compared with data from nearby dual-loop detectors. It is shown that the RTMS measures are noisier than loop detectors for occupancy (percentage of time the detector is occupied by vehicles) and flow (number of vehicles per unit time), although the RTMS velocity estimates are almost as good as those from single-loop detectors (but inferior to direct measurement from dual-loop detectors). Second, the study considers aggregate measurements from contact closure data and compares the RTMS against the dual-loop detectors. For reference, the work also compares one loop against another in a dual-loop detector, with the spacing between loops being greater than the spacing between the reference loops and the RTMS detection zone.

01014803

English

pp 149-163

2005

Transportation Research Record: Journal of the Transportation Research Board

0309093902

Print

Figures (9) ; Photos (1) ; References (17) ; Tables (3)

Evaluation and assessment; Freeways; Loop detectors; Microwave detectors; Noise; Performance; Speed measurement

Environment; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Dec 27 2005 3:42PM

• Assessing Issues, Technologies, and Data Needs to Meet Traffic Input Requirements by Mechanistic-Empirical Pavement Design Guide: Implementation Initiatives
• Assessing Robustness of Imputation Models Based on Data from Different Jurisdictions: Examples of Alberta and Saskatchewan, Canada
• Comparison of Trip Determination Methods in Household Travel Surveys Enhanced by a Global Positioning System
• Connectionist Approach to Improving Highway Vehicle Classification Schemes: The Florida Case
• Cost-Effective Reporting of Travel on Local Roads
• Customized Pretrip Prediction of Freeway Travel Times for Road Users
• Developing Custom Tools for Deriving Complex Data from Travel Survey Databases
• Enterprisewide Spatial Data Integration of Legacy Systems for Asset Management: The Case of the Illinois Department of Transportation
• Estimating Intersection Control Delay Using Large Data Sets of Travel Time from a Global Positioning System
• Evaluation of Microwave Radar Trailers for Nonintrusive Traffic Measurements
• Experience Implementing a User Service for Archived Intelligent Transportation Systems Data
• Exploiting Correlations Between Link Flows to Improve Estimation of Average Annual Daily Traffic on Coverage Count Segments: Methodology and Numerical Study
• Factors Affecting Minimum Number of Probes Required for Reliable Estimation of Travel Time
• Improved Dual-Loop Detection System for Collecting Real-Time Truck Data
• Monitoring and Predicting Freeway Travel Time Reliability: Using Width and Skew of Day-to-Day Travel Time Distribution
• Probabilistic Vehicle Identification Techniques for Semiautomated Transportation Security
• Reality-Based Approach to Stated Preference Surveys
• Traffic Measurement and Vehicle Classification with Single Magnetic Sensor
• Trip-Chaining Trends in the United States: Understanding Travel Behavior for Policy Making
• Two Minutes per Person per Day Each Year: Exploration of Growth in Travel Time Expenditures
• Vehicle Classification Model for Loop Detectors Using Neural Networks