The expanding deployment of emerging technologies such as connected vehicles (CVs), automated vehicles, shared mobility, and smart cities and communities, has increased demand for application of relatively new testing and implementing methods, encompassing field operational tests (FOTs), trials, and model deployments. Such methods entail extensive data collection from real users operating on public roads. While the databases previously developed have been curated and analyzed for specific project purposes, broad collaboration has not yet occurred for the purpose of informing state and local agencies of lessons learned and best practices in deployment.
The range of such deployments has recently expanded to include U.S.DOT’s CV Pilot Deployment Program, the Smart City Challenge, and the Advanced Transportation and Congestion Management Technologies Deployment program of FHWA. These programs are convening collaborative actions that include AASHTO’s Vehicle-to-Infrastructure Deployment Coalition , the CV Pilots Technical Roundtable, and AASHTO’s Signal Phase & Timing Challenge. These leading-edge activities are addressing technical challenges to deployment, including cybersecurity; however, there is a residual need for collaborative data analytics to address a large number of policy questions of relevance to a broad range of state and local public agencies.
It is generally agreed that the coming decade will see rapid changes in transportation, and state and local transportation agencies are eager to benefit from the experiences of early adopters. The NHTSA Notice of Proposed Rule Making for vehicle-to-vehicle communication provides definite time frames for wide deployment of connected vehicle technology. Further expansion of CV and smart cities pilots is assured, and the proposed framework for data analytics will help streamline these deployments.
The objective of this research is to develop a framework for aggregating data from CV pilot deployments and smart city initiatives to assist in the development of guidance for transportation policy. The CV information aggregation framework needs to take into account the full range of policy questions relevant to agencies at the state and local levels, as affected by policies at the federal level. The framework needs to include a taxonomy of data collection activities and a generally applicable data curation model. The ability of this data model to address the range of policy questions needs to be considered, and gaps need to be identified. Finally, a comprehensive, prioritized set of accelerated, dynamic research projects, using the data to address suites of policy issues, needs to be developed and documented. Pilot deployments are well under way and are expected to proliferate. Agencies will benefit greatly from the lessons learned, particularly as these lessons improve efforts to increase portability of the information.