Benton Harbor Smart Mobility Project Community Report

Abstract

Residents of many American communities face mobility challenges reaching everyday destinations, such as workplaces, schools, medical offices and grocery stores. In these communities, public transportation systems provide a vital lifeline for their communities, but communities often lack the resources, methodologies, and capacity to undertake improvements to these systems. The objective of this project was to research how under-resourced communities can utilize smart and connected community technologies to implement novel but lean solutions to improve long-standing community mobility challenges.

The project team investigated this topic through a multi-faceted engagement with the community of Benton Harbor, Michigan, including the city’s public transit provider, the Twin Cities Area Transportation Authority, and many community residents and stakeholders. Project activities fell into three general categories:

1. The collection and analysis of data from multiple sources: resident travel surveys, rider and stakeholder interviews, transit system administrative logs, and transit vehicle tracking;

2. The creation of analysis, modeling, visualizations, and tools from the data sources to yield insights useful for service planning and operational improvement;

3. The design and implementation of a collaborative scenario planning process to allow community stakeholders to learn from and utilize the data collected to create proposals for transit service improvements.

A hallmark of the project was an effort to boost community capacity, and it featured the extensive sharing of data, expertise, and tools. Supplemental funding allowed for the conversion of a research vehicle tracking system into an operational asset for the agency, and also resulted in the agency hiring a new permanent staff member. Scholarly outputs of the project include 18 peer-reviewed articles or paper presentations covering innovations in computer vision, travel surveying, transit system performance, travel demand modeling, and scenario planning