Share Email Print
cover

Proceedings Paper

Nonparametric approach to classification using neural networks
Author(s): David W. Elizandro; Scott A. Starks; Octavio Lerma-Sanchez; Vern With
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Accurate estimates of traffic characteristics are essential for effective highway planning and management. This paper briefly describes the current approach, based on FHWA recommendations, to estimating these traffic characteristics and recommends an alternative approach that has the potential for improving the precision of the estimates and/or reducing the data collection efforts and their corresponding costs. The alternative recommendation is based on the concept of linear transfer functions for leading indicators to forecast estimates of the traffic characteristics.

Paper Details

Date Published: 1 November 1992
PDF: 8 pages
Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); doi: 10.1117/12.131591
Show Author Affiliations
David W. Elizandro, East Texas State Univ. (United States)
Scott A. Starks, Univ. of Texas/El Paso (United States)
Octavio Lerma-Sanchez, Univ. of Texas/El Paso (United States)
Vern With, National Highway Safety Administration (United States)


Published in SPIE Proceedings Vol. 1826:
Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top