Advances in Uncertainty Treatment in the FHWA Procedure for Estimating Annual Average Daily Traffic Volume

This paper proposes an approach that estimates Annual Average Daily Traffic (AADT) of a road section from limited data. This approach attempt to preserve the FHWA current procedure, but its objective is to improve the accuracy and the interpretability of the results. When the short period traffic count (SPTC) on a road section is given, a road group (whose AADT is known) which has the similar traffic pattern is identified. The AADT of the road section in question is estimated by adjusting the AADT associated to the road group by the degree of similarity. The uncertainty associate with the degree of similarity is measured by non-specificity and discord. The model’s performance is tested with data obtained at 50 permanent counting sites in the Province of Venice, Italy. The analysis considers the characteristics of SPTC, including duration and day of the week. The estimates using the proposed methods and two existing methods are compared. The proposed method is found to produce more accurate results than the previous method. Forty-eight hour short counts taken on weekdays are found to be the best sample SPTC for AADT estimation. Furthermore, the measures of uncertainty help interpret the quality of the estimates and also indicate the need for additional data. SPTCs with a lower value of discord are found to yield the better AADT estimates, while a high value of non-specificity measure indicates uncertainty with respect to match with the groups. These measures can also indicate the need for additional data collection.