Neural Network Optimal Model for Classification of Unclassified Vehicles in Weigh-in-Motion Traffic Data

Abstract

A weigh-in-motion (WIM) system has the capability to perform on-site vehicle classifications based on the FHWA schema. However, WIM datasets often contain a significant portion of vehicles that could not be classified into any of the 13 vehicle classes by WIM devices. Possible reasons for the WIM classifier failing to classify these vehicles are tailgating, lane changing, traffic congestion, and equipment malfunction. Analysis of unclassified vehicles was performed with WIM-recorded data. A neural network model was established to determine the appropriate allocations of unclassified vehicles to vehicle classes. Since the number of unclassified vehicles is often fairly high, the allocations will help to improve the accuracy of truck traffic data and thus improve pavement design. Video records of traffic streams on an interstate section and traffic data from a nearby WIM station were used to identify causes for vehicle misclassifications. The optimal model was developed through model algorithm design, data processing, model training, validation, robustness analysis, and verification of video records. It was found that the optimal model was effective in allocating unclassified vehicles to appropriate vehicle classes. The optimal model was able to reclassify the unclassified vehicles that had non-zero attributes with high accuracy. The optimal model provides a useful tool for properly allocating the unclassified vehicles to the FHWA specified vehicle classes. The developed allocations can be applied to allocate unclassified vehicles appropriately to vehicle classes for pavement design and would potentially increase benefit and reduce cost with reliable and realistic pavement designs.