01014806

Component

https://doi.org/10.3141/1917-20

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

The mechanistic-empirical pavement design methodology being developed under NCHRP Project 1-37A will require accurate classification of vehicles to develop axle load spectra information needed as the design input. Scheme F, used by most states to classify vehicles, can be used to develop the required load spectra. Unfortunately, the scheme is difficult to automate and is prone to errors resulting from imprecise demarcation of class thresholds. In this paper, the classification problem is viewed as a pattern recognition problem in which connectionist techniques such as probabilistic neural networks (PNN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. The PNN was developed, trained, and applied to field data composed of individual vehicles’ axle spacing, number of axles per vehicle, and overall vehicle weight. The PNN reduced the error rate from 9.5% to 6.2% compared with an existing classification algorithm used by the Florida Department of Transportation. The inclusion of overall vehicle weight as a classification variable further reduced the error rate from 6.2% to 3.0%. The promising results from neural networks were used to set up new thresholds that reduce classification error rate.

01014803

English

pp 182-189

2005

Transportation Research Record: Journal of the Transportation Research Board

0309093902

Print

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

Accuracy; Axle load force; Mechanistic design; Neural networks; Pattern recognition systems; Vehicle classification; Vehicle weight

Axle spacing

Florida

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

TRIS, TRB, ATRI

Dec 28 2005 8:17AM

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• Exploiting Correlations Between Link Flows to Improve Estimation of Average Annual Daily Traffic on Coverage Count Segments: Methodology and Numerical Study
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• 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
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• Trip-Chaining Trends in the United States: Understanding Travel Behavior for Policy Making
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