01155115

Component

https://doi.org/10.3141/2160-09

http://scholar.google.com...sumo&publication_year=2010

The collection of vehicle classification data, and of heavy goods vehicle or truck data in particular, is fundamental to the design and maintenance of transportation infrastructure. Classification monitoring efforts also improve the availability and reliability of volume data, which are the backbone of traffic analyses and transportation management systems. Nonintrusive sensors minimize the adverse impact on traffic and surrounding communities (e.g., closing traffic, cutting the pavement) when installed and maintained. The application of nonintrusive sensors for vehicle classification is possible for a simplified scheme of classes, and accuracy expectations improve, along with the rapid development of the nonintrusive technology. Three sensors were tested to evaluate the accuracy and reliability of vehicle classification: Autoscope RackVision Terra, a length-based classification sensor utilizing video imaging technology; the Infra-Red Traffic Logger (TIRTL), an axle-based classification sensor utilizing active infrared technology; and SmartSensor HD, a length-based classification sensor utilizing microwave radar technology. The accuracy of traffic sensors for vehicle classification was established with simultaneous field observations (direct or videotaped) or comparison of classification data collected simultaneously by different sensors. The conclusion is that only TIRTL can provide reliable classification under ideal conditions, while Autoscope can be accurate for some simplified classes. The SmartSensor HD did not provide good class counts on the basis of various deployments in Honolulu, Hawaii.

01173399

10-1846

English

pp 77-86

2010

Transportation Research Record: Journal of the Transportation Research Board

9780309142885

Print

References (7) ; Tables (9)

Automatic vehicle classification; Field tests; Infrared detectors; Microwave detectors; Sensors; Vehicle classification; Video imaging detectors

Honolulu (Hawaii)

Data and Information Technology; Highways; Operations and Traffic Management; I73: Traffic Control

TRIS, TRB, ATRI

Jan 25 2010 10:51AM

• Approach to Collecting Local Freight Information
• Clustering Analysis to Characterize Mechanistic–Empirical Pavement Design Guide Traffic Data in North Carolina
• Combined-Cost and Bi-Objective Approaches to Multipurpose Location-Based Services Using Genetic Algorithms
• Data Collection of Freeway Travel Time Ground Truth with Bluetooth Sensors
• Developing an Asset Management Tool to Collect and Track Commitments on Environmental Mitigation Features
• Development of Data Collection and Integration Framework for Road Inventory Data
• Effect of Axle Load Measurement Errors on Pavement Performance and Design Reliability
• Effects of Temporal Data Aggregation on Performance Measures and Other Intelligent Transportation Systems Applications
• Empirical Evaluation of New Robust Travel Time Estimation Algorithms
• Project-Level Life-Cycle Benefit–Cost Analysis Approach for Evaluating Highway Segment Safety Hardware Improvements
• Simulation and Evaluation of Automated Vehicle Identification at Weigh-in-Motion Inspection Stations: Case Study from British Columbia, Canada
• Synthesis Framework for Generating County-Level Freight Data Using Public Sources for Spatial Autocorrelation Analysis
• Techniques for Validating an Automatic Bottleneck Detection Tool Using Archived Freeway Sensor Data
• Traffic Monitoring of Motorcycles During Special Events Using Video Detection
• Transportation Asset Management Systems and Climate Change: Adaptive Systems Management Approach
• Volume Data Correction for Single-Channel Advance Loop Detectors at Signalized Intersections