Abstract
Weigh-in-motion (WIM) devices classify each passing vehicle into one of the 13 types of vehicles defined by the Federal Highway Administration according to the axle configurations of the vehicle. Accurate vehicle classifications are pivotal for pavement design as they are the main parts of the required design input. However, it is often found that there exist sizable misclassified vehicles in the WIM recorded data. These misclassified vehicles would have major adverse effects on pavement design. This paper presents a study using a machine learning algorithm to identify misclassified vehicles and reclassify them into the appropriate vehicle categories. A video captured near a WIM station was utilized in combination with the WIM recorded data in the machine learning algorithm. The main features of the correctly classified vehicles and the misclassified vehicles were identified and utilized in the training and testing procedures. It proved in this study that the machine learning method could identify the majority of the misclassified vehicles and placed them into the correct vehicle groups. Consequently, the accuracy of the WIM recorded traffic data can be significantly improved, and the quality of pavement design can be greatly enhanced.
