Detect and charge: Machine learning based fully data-driven framework for computing overweight vehicle fee for bridges

Abstract

Abstract This study develops a fully data-driven framework for computing overweight vehicle fee that combines historical bridge data from National Bridge Inventory (NBI) and weigh-in-motion (WIM) data. In this framework, information regarding vehicle weight distribution on bridges was obtained using Gaussian mixture model (GMM) based interpolation. Using this interpolation approach, the vehicle weight distribution on each bridge could be estimated from WIM data based on their location. Later, these estimated distributions were combined with the NBI for developing a machine learning-based prediction model that inputs bridge characteristics (e.g., age and traffic) and outputs deck condition. The model was employed to calculate the expected bridge service life under two scenarios to compute a bridge life reduction per damaging load. Finally, the bridge life cycle cost was conducted to convert the calculated service life difference into a fee. Integration of this framework with existing geographical information system based online permit issuing tools will allow for detection of bridges on vehicles' routes and charge them a fee considering their weight and the load capacity of the bridges they will pass over. Therefore, fees will be calculated more accurately and efficiently. Additionally, the proposed framework has the flexibility of being converted into a table for conforming to the conventional permit fee calculation scheme.