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A vehicle classification algorithm is applied to weigh-in-motion data to model long multiple trailer truck exposure. Long trucks are specially permitted truck configurations, consisting of van trailers or containers, which exceed basic vehicle length limits but operate within basic weight restrictions. Despite widespread use of these trucks for many years, there is a knowledge deficiency about their exposure. The algorithm provides the core dataset for modelling long-truck exposure in terms of the volume of trips, and their weight and cubic characteristics. It is embedded within a modelling approach in which exposure is an explanatory variable needed for predicting transportation system impacts related to long-truck operations. These impacts are considered latent variables, which are represented by observable performance indicators. Integration of trucking industry intelligence into the model enables the interpretation of patterns and anomalies in the data. Illustrative model results are provided and the model is validated by testing the reasonableness of its response against expected results, given actual transportation system conditions. An illustrative application demonstrates the model's capability to help predict impacts in the road safety context. Although the results and application pertain to long trucks, the model structure and definition are generic and valid for any trucking sector.