Principal Investigator: Shamsnaz Virani Bhada

Collaborators: Constance Clark, Jennifer deWinter, Paul P Mathisen

This project brings together two forms of digitized information to analyze complex policy information as it intersects with green initiatives and provide data-driven recommendations for smart city development and growth.

The first is Machine Readable Policy Models: Smart Connected Complex Systems such as health care or transportation are governed by a mosaic of policy documents that are Ambiguous, Vague, Complex, Verbose and Inconsistent. Inaccurate, Inconsistent and Unclear Policies lead to Systemic Failures such as Veteran Affairs Health Care Inconsistencies or failures in public transportation. Policies current and future need to be traceable, analyzable and digitized to reduce systemic failures for engineered systems. Policy Content Digitization and Analysis therefore provides a key touchstone in our research. We use systems approaches to build a Machine Readable Policy Models that are Analyzable, Traceable and Descriptive. The policy digitization framework represents the conceptual view supported by a step by step approach to achieve complete policy digitization and analysis.

The second is simulation modeling of complex systems: Complex systems often emerge from the constraints imposed by economic, social, behavioral, technological, historical infrastructure, governmental, and historical exigencies. Policy decisions, then, are often created based on current usage data with little way to test shifts in the system within new contexts. Transportation, for example, is a key initiative in green cities and smart world infrastructure; however, the perception is that ridership is in decline and thus necessitates a reduction in service. Successful models in other locations are often dismissed, often for economic reasons, because the model of change is difficult to represent in new locations. Modeling and simulation uses mathematical and spatial representations of complex systems to test controllable variables in controlled systems.

This project will bring together Machine Readable Policy Models and Simulation Modeling together through the test case of public transportation in Worcester. Worcester City Council has a Green Worcester plan in place, and public transportation is a key topic that is eluding action from policy makers. The team will work to analyze current interconnected policies, regulations, tax structures, available technologies. Following proposed smart connected systems, the team will build a city simulation model for public transportation to test possible solutions, with particular emphasis on race, gender, and socio-economic backgrounds, as well as key demographics around access to healthcare, education, work, and downtown 18-hour revitalization, collecting and analyzing data from different modeling. Our data analysis of the simulation will then inform the policy content modeling for policy recommendations put forward toward smart city planning and implementation.