ACME specializes in performing economic analysis and performance evaluation of autonomous and connected transportation solutions. This program leverages academic and technical resources of the College of Engineering and the Center for Urban Transportation Research. Researchers specialize in advanced econometric methods, traffic engineering and safety, data mining, machine learning and artificial intelligence applications to produce quick response solutions to better inform practitioners and policy maker in selecting and prioritizing cost-feasible alternatives.
With an emphasis on institutional innovation, ACME assists transit agencies, local, state and federal authorities with policy development, operational enhancements, and exploration of new technologies. ACME’s projects cover these main areas of research:
Infrastructure Investment and Economic Development – ACME bring more than 20 years of experience in conducting economic impact analyses and performance assessments of public transportation, airport and roadway projects. The group’s analytical expertise helps organizations assess the economic impacts and effectiveness of policy, planning, and investment decision making.
The Economic and Social Consequences of Connected and Autonomous Vehicles – ACME evaluates the evaluation and performance assessment of connected and autonomous vehicles in terms of safety, mobility, economic productivity, energy and environmental benefits. ACME is currently leading the performance evaluation and assessment the Federal Highway Administration (FHWA) Connected Vehicle Pilot Deployment Program – Tampa Pilot.
Travel Behavior, Land Use and Urban Form – ACME researchers focus on theoretical modeling of the relationship between urban form, residential location and travel patterns. By formulating implementable models that define how household travel behavior responds to changes in transportation infrastructure.
Transportation Demand Management Evaluation (TDM) – ACME specializes on the development and implementation of practitioner-oriented applications to quantify the societal costs and benefits of congestion-reduction strategies with a focus on energy conservation.
The underlying interest in these areas is in providing scientific investigation to evaluate mechanisms supporting livable and sustainable communities.
Mehrad Eslami is a Computer Science Ph.D. candidate at the University of South Florida. After receiving his Master’s degree in Artificial Intelligence, he started working as a database administrator and data analyzer for a few years. Thereafter, he decided to take his academic education to the next level as he joined the database research group in the Computer Science department at USF. While he was pursuing his Master’s degree in Computer Science, he had been collaborating on a project with the Nursing department to monitor and track their patients’ activities. Presently, he is working at CUTR on massive data processing in databases on Phase I of the Connected Vehicle Pilot Deployment program, Tampa Hillsborough Expressway Authority. He is working under the supervision of Dr. Sisinnio Concas.