01031355

Component

https://doi.org/10.3141/1968-14

http://scholar.google.com...eton&publication_year=2006

The accurate estimation of flow speeds with single loop data is critical to freeway traffic management. Many existing methods cannot accurately estimate speed because of difficulty in determining vehicle lengths and because some assumptions can be applied only to certain limited conditions. Moreover, excessive experimentally determined parameters of some proposed approaches are difficult to implement. A new unscented Kalman filter method of speed estimation is presented. The algorithm of the proposed method is implemented and evaluated with the use of field data from Texas Transportation Institute’s vehicle detector test beds on State Route 6 in College Station, Texas, and on Interstate 35 in Austin, Texas. Estimated speeds are compared with observed speed data and with results from other estimation methods. The results indicate that the proposed method has excellent estimation accuracy and outperforms other methods. In addition to its superior accuracy, the proposed method is fairly easy to implement and practical for real-system implementation.

01037954

English

pp 117-125

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099773

Print

Figures (9) ; References (15) ; Tables (1)

Accuracy; Algorithms; Estimating; Freeway management systems; Kalman filtering; Loop detectors; Speed; Speed data

Single loop detectors

Texas

Highways; Operations and Traffic Management; I71: Traffic Theory; I73: Traffic Control

TRIS, TRB, ATRI

Mar 3 2006 11:05AM

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• Frameworks to Represent Uncertainty When Level of Service Is Determined
• Improving the Prediction of Annual Average Daily Traffic for Nonfreeway Facilities by Applying a Spatial Statistical Method
• Incorporating Wavelet Decomposition Technique to Compress TransGuide Intelligent Transportation System Data
• Kalman Filter-Based Tracking System for Automated Inventory of Roadway Signs
• Moments Analysis for Improving Decision Reliability Based on Travel Time
• Performance Evaluation of Sensor- and Image-Based Technologies for Automated Pavement Condition Surveys
• Predicting Directional Design Hourly Volume from Statutory Holiday Traffic
• Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters
• Real-Time Detection of Crash-Prone Conditions at Freeway High-Crash Locations
• Travel Time Estimation with Correlation Analysis of Single-Loop Detector Data