Characterization of Business Establishments and Commercial Vehicle Movements Utilizing Machine Learning Techniques in Halifax, Canada

Abstract

This thesis develops a commercial vehicle travel demand forecasting model. First, it characterizes business establishments to investigate the agglomeration of businesses. Next, it develops location choice models for five major business types. Additionally, the thesis develops commercial vehicle trip generation models, considering the agglomeration phenomenon of businesses. These models are then utilized to develop a commercial vehicle travel demand forecasting model for Halifax Regional Municipality (HRM). Furthermore, a shopping destination choice model for activity models is developed, which will be implemented within an integrated transport, land use, and energy (iTLE) modeling system to improve the behavioral representation of destination choices. The uniqueness of this study lies in its novel approach to extracting agglomeration insights to develop commercial vehicle trip generation models. The findings of this study offer valuable insights for commercial vehicle movements, integrated urban system modeling, and policymaking concerning economic development and the growth of the urban built environment.