The concept of truck travel demand forecasting, internal to a region, has always been built upon modeling discrete truck trip ends, distributing truck trip ends to various origins and destinations using travel time impedances and some land use characteristics, and allocating truck trip tables into distinct time periods using factors derived from observed counts. An innovative enhancement to this approach is to apply activity-based modeling (ABM) principles to truck tour characteristics and develop a tour-based truck travel demand model. This paper focuses on two aspects – (a) processing of truck GPS data, and (b) developing a tour-based truck model. The processing of truck GPS data is done for the MAG region to construct a truck tour database necessary for estimating tour-based models. The tour-based models include stop generation and purpose models, and time period allocation and duration models to predict the occurrence of truck stops in space and time for each industry sector. This paper also discusses the calibration and validation of these discrete choice models that are linked together to output trip chains or truck tours for different industry sectors.
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