Data-Informed Scenario Planning for Mobility Decision Making in Resource Constrained Communities
Lead PI:
Robert Goodspeed

The rapid emergence of new information and sensing technologies is empowering the formation of smart and connected communities (S&CC). This project aims to advance the use of smart and connected technologies to empower new modes of community-based decision making to identify and implement transformative solutions to community challenges. The project focuses on resource-constrained communities. The team will offer the community of Benton Harbor, Michigan, tools needed to explore new mobility solutions that provide greater access to employment, education, and healthcare. The project deploys sensing technologies to collect data needed to create analytical models of resident mobility preferences and mobility service performance. A community-based decision making framework will be created using scenario planning methods; in this framework, stakeholders are provided tools to explore mobility solutions with predicted outcomes visualized. Included in the team is the Twin Cities Area Transportation Authority (TCATA), which will iteratively implement mobility solutions originating from the scenario planning process with solutions quantitatively assessed. A partnership with Lake Michigan College further enhances the project's broader impacts by engaging community college students in the research and offering them experiences in the smart city field of study.

To explore the fundamental question of how resourced-constrained communities can utilize smart and connected technologies to implement novel but lean solutions to mobility challenges, the project will define a cost-effective data collection strategy that can assess the performance of existing solutions, track the mobility patterns of residents, and acquire resident perceptions of their mobility. GPS tracking using cell phones apps and computer vision on city buses will be used to generate the data needed to model the performance of current mobility configurations. Surveys of residents will augment these data sources. The project will map mobility data to an analytical framework that can predict both resident demand for mobility services and the performance of these services given changes in user demand. Activity-based models will be created with special emphasis on fine-grain estimation of travel demand in small communities. Predictive models will be developed to predict the quality of transit services provided by configurations of the mobility network. A key advancement will be the creation of scalable computational methods that optimize the mix of fixed route service with on-demand shuttling. This project will enable community-based decision making by visualizing mobility data and predictive outputs during a participatory planning process. The team will also provide TCATA with the ability to track and iteratively shape public transportation in order to enhance access to employment, healthcare, and education outcomes.


We documented our stakeholder engagement process in the 2023 paper in Case Studies in Transport Policy, "Improving transit in small cities through collaborative and data-driven scenario planning."

Robert Goodspeed
Performance Period: 09/01/2018 - 08/31/2024
Institution: Regents of the University of Michigan - Ann Arbor
Award Number: 1831347