The paper deals with the new method of automatic vehicle classification called ALT (ALTernative). Its characteristic feature is versatility resulting from its open structure, moreover a user can adjust the number of vehicles and their category according to individual requirements. It uses an algorithm for automatic vehicle recognition employing data fusion methods and fuzzy sets. High effectiveness of classification while retaining high selectivity of division was proved by test results. The effectiveness of classification of all vehicles at the level of 95% and goods trucks of 100% is more than satisfactory.

The paper provides analysis of the influence of temperature on the error of weigh-in-motion (WIM) systems utilizing piezoelectric polymer load sensors. Results of tests of these sensors in a climatic chamber, as well as results of long-term tests at the WIM site, are presented. Different methods for correction of the influence of changes in temperature were assessed for their effectiveness and compared.