Abstract Summary
Agent-based freight models are used to simulate a plethora of agents, agent attributes, and operational environments. The population in these models typically comprises business establishments or firms. Due to lack of data, attributes are often limited to readily available traits such as firm size and industry category. This research addresses the data gap issue by building a data development engine (DDE). The DDE extracts key company data from big, online, text-based information systems and builds a dataset of companies to use in model estimation. The primary, initial focus of the DDE is to develop data regarding strategies that are adopted by companies and used to guide company decisions. Earlier work has shown that including firm strategies in an agent-based freight model is important for estimating freight agent behavior and, ultimately, impacts on energy use and vehicle-miles traveled. Factor Analysis and Structural Equation models are used to identify firm emphasis areas and a latent variable that weighs the relative importance of two emphasis areas (service vs. product innovations). The resulting strategic data is used to inform a model of private fleet ownership.
