Neural Networks as Alternative to Traditional Factor Approach of Annual Average Daily Traffic Estimation from Traffic Counts

Abstract

Presented in this paper is a comparison of the neural network approach and the traditional factor approach for estimating annual average daily traffic (AADT) from 48-h sample traffic counts. Minnesota’s automatic traffic recorder (ATR) sites are investigated. The traditional AADT estimation approach involves application of volume adjustment factors to sample counts. The neural network model used in this study is based on a multilayered, feed-forward, and back-propagation design for supervised learning. The results of AADT estimation from a single shortperiod traffic count indicate that as compared with the neural network approach, the estimation errors for the factor approach can be lower under a scenario in which ATR sites are grouped appropriately and the sample sites are correctly assigned to various ATR groups. Unfortunately, the current recommended practice offers little guidance on how to achieve the assignment accuracy that may be necessary for obtaining reliable AADT estimates from sample counts. The advantage of the neural network approach is that classification of ATR sites and sample site assignments are not required. The neural network approach can be particularly suitable for estimating AADT from two or more shortperiod traffic counts taken at different times during the counting season.

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