A method to correlate weigh-in-motion and classification data

摘要

This paper describes a method that uses low-cost vehicle classifiers to provide an indication of pavement loading or gross vehicle mass (GVM). The proposed methodology identifies, from a list of candidate weigh-in-motion (WIM) sites (therefore with known GVM frequency distributions), the one that can give the best indication of the GVM distribution at a classifier site. This classifier site needs to be equipped with an intelligent classifier that has a sensor to indicate the level of unladenness. The method consists of two stages. The first stage is used to determine whether the loading characteristics for a vehicle class in a jurisdiction are suitable for correlating classified counts with WIM data. It is based on the analysis of GVM cumulative frequency distributions of WIM sites and the use of the Kolmogorov-Smirnov Statistic (KSS). The second stage is used to identify the best site from a list of candidate WIM sites to match the data at an intelligent classifier site, if the loading characteristic of that jurisdiction is found suitable. The method was found robust and the analyses using WIM data from Queensland produced the right matches.