SCC: Leveraging Autonomous Shared Vehicles for Greater Community Health, Equity, Livability, and Prosperity (HELP)
Principal Investigator(s):Zhi-Li Zhang, Professor, Computer Science and Engineering
This Smart and Connected Communities (SCC) grant supports fundamental research on a critical challenge facing many cities and communities: how to leverage the emerging autonomous vehicles (AVs) to re-think and re-design future transportation services and enable smart and connected communities where everyone benefits. The research envisages an ambitious "smart cloud commuting system" (SCCS) based on giant pools of shared AVs. The envisaged SCCS has the potential to bring about far-reaching societal changes. It will provide inexpensive mobility services to all people--especially people with socio-economic disadvantages--help build stronger family and community ties, and boost economic productivity and equity by mitigating or removing mobility constraints. This research is being carried out in conjunction with five community engagement pilot projects and is directly contributing to U.S. prosperity and well-being. The research involves multiple disciplines, including transportation, computer science, data science, operations research, urban design, and public policy. The multi-disciplinary approach is helping broaden participation of underrepresented groups in research, and it is enriching students' educational experience across science, engineering, urban design, and public policy.
The goal of the project is two-fold: (1) to study the feasibility, economic viability, architectural and operational designs of the envisaged SCCS; and (2) to analyze the socioeconomic challenges in realizing the envisaged SCCS to serve communities with diverse socioeconomic backgrounds. In support of these goals, the project is leveraging new and emerging data on travel demand, user preferences, and activity-travel constraints to quantify system efficiency gains that can be attained from time-sharing and intelligent control of AVs as well as from ride-sharing and smart trip scheduling of users. The research is also developing optimization models and algorithms that account for essential tradeoffs, including cost, quality of service, and congestion, in deciding how best to deploy AVs geographically and temporally. This leads to the identification of optimal AV fleet architectures and optimal operational policies. The research is also investigating, using micro-economic/game-theoretic analysis of the incentives of both users and service providers, likely scenarios of vehicle ownership and market structures and studying the impact of each scenario on traffic measures including vehicle ownership and traffic volumes as well as societal measures including community health, equity, livability, and prosperity. This research is generating fundamental knowledge on the socioeconomic opportunities and impacts of the envisaged SCCS with shared AVs, and it is developing guidelines for adapting the design, deployment, and operation of AVs for future smart cities.