Connectionist Approach to Improving Highway Vehicle Classification Schemes

Abstract

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.

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References

FHWA. Traffic Monitoring Guide, Section 4: Vehicle Classification Monitoring. Office of Highway Policy Information, U.S. Department of Transportation, 2001.

Wyman

J. H.

, Braley

G. A.

, and Stevens

R. I.

. Field Evaluation of FHWA Vehicle Classification Categories. Report No. DTFH-71-80-54-ME-03. FHWA, U.S. Department of Transportation, 1985.

Guide for Design of Pavement Structures, AASHTO, Washington, D.C., 1986.

Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures. Final Report, 2004. NCHRP Project 1–37A. www.trb.org/mepdg/guide.htm.

Cottrell

B. H.

Jr. , Schinkel

T. O.

, and Clark

T. M.

. A Traffic Data Plan for Mechanistic–Empirical Pavement Designs (2002 Pavement Design Guide). Final Report VTRC 04-R3. Virginia Transportation Research Council, Charlottesville, 2003.

Specht

D. F.

Probabilistic Neural Networks.

Neural Networks, Vol. 3, No. 1, 1990, pp. 109–118.

Specht

D. F.

Probabilistic Neural Networks and the Polynomial Adaline as Complementary Techniques for Classification.

IEEE Transactions on Neural Networks, Vol. 1, No. 1, 1990, pp. 111–121.

Tsoukalas

L. H.

, and Uhrig

R. E.

. Fuzzy and Neural Approaches in Engineering. John Wiley and Sons, New York, 1997.

Duda

O. R.

, Hart

P. E.

, and Stork

D. G.

. Pattern Classification. John Wiley and Sons, New York, 2001.

10.

Bishop

C. M.

Neural Networks for Pattern Recognition. Oxford University Press

New York,

1995.

11.

Vapnik

N. V.

The Nature of Statistical Learning Theory, 2nd ed. Springer-Verlag, New York, 2000.

12.

Burges

J. C. C.

A Tutorial on Support Vector Machines for Pattern Recognition.

Data Mining and Knowledge Discovery, Vol. 2, 1998, pp. 121–167.

13.

Bose

N. K.

, and Liang

. Neural Network Fundamentals with Graphs, Algorithms, and Applications. McGraw-Hill, New York, 1996.

14.

Michie

, Spiegelhalter

D. J.

, and Taylor

C. C.

. Machine Learning, Neural and Statistical Classification. Ellis Horwood Publishing

Hertfordshire, United Kingdom,

1994.

15.

Parzen

On Estimation of a Probability Density Function and Mode.

Annals of Mathematical Statistics, Vol. 33, 1962, pp. 1065–1076.

16.

Murthy

V. K.

Estimation of a Probability Density.

Annals of Mathematical Statistics, Vol. 36, 1965, pp. 1027–1031.

17.

Cacoullous

Estimation of a Probability Density.

Annals of the Institute of Statistical Mathematics, Vol. 18, No. 2, 1966, pp. 179–189.