The objective of this research is to understand the complexities associated with integration between humans and cyber-physical systems (CPS) at large scales. For this purpose, the team will develop and demonstrate the application of Smart City Hubs focusing on intelligent transportation services in urban settings. Ultimately, this project will produce innovative tools and techniques to configure and deploy large-scale scale experiments enabling the study of how humans affect the control loops in large CPS such as smart cities. This work covers several design concerns that are specific to human-CPS such as human computer interfaces, decision support systems and incentives engineering to keep humans engaged with the system. The technology base will include a novel integration platform for allowing (1) integration of spatially and temporally distributed sensor streams; (2) integration of simulation-based decision support systems, (3) development and execution of experiments to understand how advanced decision support tools combined with incentive mechanisms improve the utilization of the transportation infrastructure and user experience. A key aspect of this research will be development of data-driven rider models that can be subsequently used by city engineers for planning purposes. The proposed system will enable a new generation of human-CPS systems where sensing, wireless communication, and data-driven predictive analytics is combined with human decision-making and human-driven actuation (driving and physical infrastructure utilization) to form a control loop. The Smart City Hub provides a generic platform for a number of other services beyond traffic and public transportation, including maps and way finding, municipal communication, emergency management and others. The tools that will be developed will allow researchers and practitioners to more quickly prototype, deploy and experiment with these CPS. To ensure these benefits, the research team will make its research infrastructure freely available as an open-source project. It will also develop educational materials focused on modeling, prototyping and evaluating these applications at scale. In addition, the studies the team will perform will provide new data and new scientific understanding of large-scale human interaction with CPS, which it expects will yield long-term benefits in the design and analysis of such applications.

It is expected that in 25 years, Americans who are 65 years or older will account for about 20% of the whole population. As smart cities are also expected to become a reality within the same timeframe, starting to address the needs and concerns of such a large group becomes an essential part of the design of a future smart city. Here we specifically address the mobility needs of the elderly and those with limited means of transportation. We consider multiple small vehicle options that might provide on-demand or scheduled means of door-to-door transportation. The NSF-EAGER project focuses on examining basic research aspects of sensing and tracking potential sources of vehicle pedestrian collisions in densely crowded situations and socially acceptable distance for collision avoidance. The project will be providing input to the OSU/Columbus Global City Teams Challenge activity SMOOTH (Smart Mobile Operation: OSU Transportation Hub) and related demonstrations and help develop a working system. The key innovative contributions of this EAGER project are: development of a unifying framework for sensing and tracking in mixed traffic situations, acceptable automated driving within pedestrian zones, and evasive road maneuvering to avoid colliding with conventional human driven vehicles.

Three emerging technologies provide unique opportunities for denser cities throughout the developed world: vehicle sharing, electric vehicles, and autonomous systems. Bringing these technologies close together can help enable joint mobility-on-demand and urban-logistics services. This project focuses on the co-development of design and algorithms to enable new concepts that will serve this purpose. The Persuasive Electric Vehicle (PEV) is a tricycle navigating in the bike lanes. The PEV can autonomously drive itself to its next customer; it can also deliver packages to its customers who order goods online. On the algorithmic front, the project will investigate (i) provably-safe algorithms for autonomous navigation in bike lanes, and (ii) algorithms for high-performance routing and rebalancing for joint mobility on demand and urban logistics. On the design front, the project will investigate (i) the vehicle-level designs that can best embrace the relevant CPS technologies, and (ii) the system-level designs and urban planning practices that can help enable the PEV concept. The PIs will collaborate with the City of Boston and participate in the Global City Teams Challenge, where they will demonstrate the PEV concept and its potential impact on future smart cities.

As self-driving cars are introduced into road networks, the overall safety and efficiency of the resulting traffic system must be established and guaranteed. Numerous critical software-related recalls of existing automotive systems indicate that current design practices are not yet up to this challenge. This project seeks to address this problem, by developing methods to analyze and coordinate networks of fully and partially self-driving vehicles that interact with conventional human-driven vehicles on roads. The outcomes of the research are expected to also contribute to the safety of other cyber-physical systems with scalable configurable hierarchical structures, by developing a mathematical framework and corresponding software tools that analyze the safety and reliability of a class of systems that combine physical, mechanical and biological components with purely computational ones. The project research spans four technical areas: autonomous and human-controlled collaborative driving; scheduling computations over heterogeneous distributed computing systems; security and trust in V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) networks; and Verification & Validation of V2X systems through semi-virtual environments and scenarios. The integrating aspect of this research is the development of a distributed system calculus for Cyber-Physical Systems (CPS) that enables modeling, simulation and analysis of collaborative vehicular systems. The development of a comprehensive framework to model, analyze and test reconfiguration, hierarchical control, security and trust differentiates this research from previous attempts to address the same problem. Educational and outreach activities include integration of project research in undergraduate and graduate courses, and a summer camp at Ohio State University for high-school students through the Women in Engineering program.

Traditionally, the design of urban transit services has been based on limited sampling data collected through surveys and censuses, which are often dated and incomplete. Lacking massive online feeds from multiple transit modes makes it hard to achieve real-time equilibrium in demand and supply relationship through cyber-control, which eventually manifests into multiple urban transportation issues: (i) lengthy last-mile transit due to non-supply, (ii) prolonged waiting due to undersupply, and (iii) excessive idle mileage due to oversupply. This project addresses these issues by focusing on two types of transportation systems in metropolitan areas: (i) public bike rental sharing systems and (ii) fleet-oriented ride sharing systems. The public bike rental sharing systems are used to allow commuters to rent bikes near public transit stations for the last mile of their trips. The fleet-oriented ride sharing systems schedule a fleet of participating vehicles for ride sharing among passengers in which shared ridership reduces individual fare paid by passengers, increases the profit of taxi drivers, and can improve the availability of service. The theory and practice of transportation sharing systems have typically focused on isolated individual transportation modes. The project will collect massive multi-modal online feeds from metropolitan information infrastructure to model dynamic behaviors of transportation systems, and then utilize massive micro-level trip information to apply fine-grained real-time control to handle rapid changes in dynamic metropolitan environments. General principles and design methodologies will be designed to build multi-modal, integrated urban transportation systems. These research discoveries will be applied toward commercial applications. Long-term deployment problem of bike stations will be addressed, especially in the low-income districts, to provide suggestions on the station deployment and assessment for specific deployment plans. The project also solves the short-term bike maintenance issue to balance the usage of shared bikes to prevent quick deterioration of rental bikes, and improve availability of bike rental services in real time. This project will also study fleet-oriented ride sharing systems that decide fares based on real-time supply/demand ratio to handle dynamic metropolitan scenarios. This project will support two Ph.D. students who will receive innovation and technology translation training through close interactions with municipal governments and small-business companies. Such partnerships expedite the adoption of cutting-edge technology, evaluate research solutions in operational environments, and obtain user feedback to trigger further innovations. The project will improve the efficiency of existing transportation systems under sharing economy and ultimately the work would noticeably improve the quality of every-day life in metropolitan areas across the United States.

One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Evolutionary algorithms (EAs), which embody a simplified computational model of the mutation and selection mechanisms of natural evolution, are known to be effective for design optimization. However, the traditional formulations are limited to choosing values for a predetermined set of parameters within a given fixed architecture. This project explores techniques, based on the idea of hidden genes, which enable EAs to select a variable number of components, thereby expanding the explored design space to include selection of a system's architecture. Hidden genetic optimization algorithms have a broad range of potential applications in cyber-physical systems, including automated construction systems, transportation systems, micro-grid systems, and space systems. The project integrates education with research by involving students ranging from high school through graduate school in activities commensurate with their skills, and promotes dissemination of the research results through open source distribution of algorithm implementation code and participation in the worldwide Global Trajectory Optimization Competition. Instead of using a single layer of coding to represent the variables of the system in current EAs, this project investigates adding a second layer of coding to enable hiding some of the variables, as needed, during the search for the optimal system's architecture. This genetic hiding concept is found in nature and provides a natural way of handling system architectures covering a range of different sizes in the design space. In addition, the standard mutation and selection operations in EAs will be replaced by new operations that are intended to extract the full potential of the hidden gene model. Specific applications include space mission design, microgrid optimization, and traffic network signal coordinated planning.

Securing critical networked cyber-physical systems (NCPSs) such as the power grid or transportation systems has emerged as a major national and global priority. The networked nature of such systems renders them vulnerable to a range of attacks both in cyber and physical domains as corroborated by recent threats such as the Stuxnet worm. Developing security mechanisms for such NCPSs significantly differs from traditional networked systems due to interdependence between cyber and physical subsystems (with attacks originating from either subsystem), possible cooperation between attackers or defenders, and the presence of human decision makers in the loop. The main goal of this research is to develop the necessary science and engineering tools for designing NCPS security solutions that are applicable to a broad range of application domains. This project will develop a multidisciplinary framework that weaves together principles from cybersecurity, control theory, networking and criminology. The framework will include novel security mechanisms for NCPSs founded on solid control-theoretic and related notions, analytical tools that allow incorporation of bounded human rationality in NCPS security, and experiments with real-world attack scenarios. A newly built cross-institutional NCPS simulator will be used to evaluate the proposed mechanisms in realistic environments. This research transcends specific cyber-physical systems domains and provides the necessary tools to building secure and trustworthy NCPSs. The broader impacts include a new infrastructure for NCPS research and education, training of students, new courses, and outreach events focused on under-represented student groups