In rural, suburban, and urban communities across the United States,
designing, developing, operating, and maintaining the mobility systems
that move people and goods is more challenging than ever. Growing
populations are placing increasing demands on transportation
infrastructure that, due to lack of upgrades, is failing to keep pace. In
many cases, "new build" options are not possible even if the vast sums
required to fund such projects were possible and supported by the
relevant multijurisdictional layers of local, state, and federal government.
Said another way, clean slates are rarities in the transportation world.
Fortunately, a range of Intelligent Transportation Systems (ITS) and other
“smart infrastructure” technologies exist to help local, state, and national
leaders better document and assess the operational realities of current
mobility systems—and, subsequently, implement data-driven
technological solutions.
A major challenge facing the implementation of ITS and other "smart"
infrastructure is not a technological problem but, rather, a social science
challenge: community mistrust and opposition to the cameras and
microsensors required to gather the vast amounts of mobility data
necessary to build and operate ITS infrastructure. Failure to address data
privacy concerns and to cultivate trust, transparency and accountability
within smart communities may lead to public backlash.
The two above-mentioned interrelated research problems—how to (1)
gather mobility data efficiently and (2) avoid violating the data privacy
and civil rights of residents—must be addressed in any successful ITS
infrastructure deployment. The proposed research, “Implementing a
Community-Based Mobility Lab: Improving Traffic, Protecting Data
Privacy,” seeks to address those two critical research problems by
simultaneously testing scalable research methods for gathering mobility
data, while conducting outreach and community-based research to
establish democratic networks of trust and transparency.