A Refined Synthetic Truck Trip Estimation Procedure for St. Louis Metropolitan Area

Increasing truck traffic has become a major contributor to highway congestion and this issue is only expected to grow at a fast pace in the future. Most regional travel demand forecast models incorporate some form of heavy truck sub-model. However, the traditional truck model is often very limited due to small data sources and a low cost to benefit ratio for modeling efforts. With more significant contribution of truck traffic to highway congestion, a more sophisticated and accurate truck model is desirable to enable regional models to better predict freight travel, traffic congestion and air quality emission for decision-makers to prepare future transportation plans. In this paper, a synthetic estimation procedure is developed to estimate truck trips and their geographic distribution in St. Louis metropolitan area, as part of the effort to improve the overall St. Louis regional model. In general, the methodology uses synthetic matrix estimation techniques to generate base year truck trip tables. More sophisticated adjustments may also be made to improve the accuracy of truck trip estimation. The procedure allows the interaction between auto and truck traffic to generate appropriate travel times in highway network, post-processes truck counts, and adjusts truck trip distribution using these travel times and truck counts. With this procedure, the truck trip generation model, applied in the base and future years, can be used to generate growth factors which are applied to the synthetic truck matrix, resulting in a future year truck demand matrix.