Traffic congestion and rising intra-city mobility are the major challenges in today's cities. Freight vehicle travels account for a portion of the rise in trips. Moreover, freight travel increases the overall cost of travel—not only in the movement of products but also in the mobility of people. Therefore, predicting freight travel demand can help predict traffic flows on specific routes and prepare for the future development of relevant infrastructure. The purpose of this study is to investigate the effect of influencing variables on the production and attraction of construction freights. The O–D Matrix has been created by information roadside interviews. The influencing variables on the production and attraction of construction freight were modeled with the Ordinary Least Squares (OLS) method, Robust Inference, and the Feasible Generalized Least Squares (FGLS) by using aggregated data in the 325 traffic analysis zones. The adjusted R-Squared of the production model estimated by OLS, robust inference, and FGLS techniques, respectively, is 0.63, 0.63, and 0.66. The adjusted coefficient of determination of the attraction model is 0.51. The results show that use of location-representative variables increase the descriptive power of construction freight production and attraction models.