Abstract
This paper presents a decision tool intended to help achieve the goal of reduction in Green House Gas (GHG) emissions in the greater Philadelphia region by the year 2050. The goal is to explore and build a pre-prototype to evaluate the value of the role for agents, alternative data sources (Census, energy reports, surveys, etc.), GIS modeling, and various social science theories of human behavior. Section 2 explains our initial research on an Agent Based Model (ABM) built upon the Theory of Planned Behavior (TPB) and the Discrete Decision Choice model (DDC) to model consumer technology adoption. The users can utilize the proposed ABM to investigate the role of attitude, social networks, and economics upon consumer choice of vehicle and transportation mode. Finally, we conclude with results on agent decisions for which transit mode to use and whether to adopt greener technologies.
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CITATION STYLE
Khansari, N., Waldt, J. B., Silverman, B. G., Braham, W. W., Shen, K., & Lee, J. M. (2017). Simulating population behavior: Transportation mode, green technology, and climate change. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10354 LNCS, pp. 172–181). Springer Verlag. https://doi.org/10.1007/978-3-319-60240-0_21
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