Recent progress in autonomous and connected vehicle technologies coupled with Federal and State initiatives to facilitate their widespread use provide significant opportunities in enhancing mobility and safety for highway transportation. This project develops signalized intersection control strategies and other enabling sensor mechanisms for jointly optimizing vehicle trajectories and signal control by taking advantage of existing advanced technologies (connected vehicles and vehicle to infrastructure communications, sensors, autonomous vehicle technologies, etc.) Traffic signal control is a critical component of the existing transportation infrastructure and it has a significant impact on transportation system efficiency, as well as energy consumption and environmental impacts. In addition to advanced vehicle technologies, the strategies developed consider the presence of conventional vehicles in the traffic stream to facilitate transition to these new strategies in a mixed vehicle environment. The project also develops and uses simulation tools to evaluate these strategies as well as to provide tools that can be used in practice to consider the impacts of automated and connected vehicles in arterial networks. The project involves two industry partners (ISS and Econolite) to help facilitate new product development in anticipation of increased market penetration of connected and autonomous vehicles. The approach will be tested through simulation at University of Florida, through field tests at the Turner Fairbank Highway Research Center (TFHRC) and through the control algorithms that also will be deployed and tested in the field. The project will support multiple graduate students and will support creation of on-line classes. The project is at the intersection of several different disciplines (optimization, sensors, automated vehicles, transportation engineering) required to produce a real-time engineered system that depends on the seamless integration of several components: sensor functionality, connected and autonomous vehicle information communication, signal control optimization strategy, missing and erroneous information, etc. The project develops and implements optimization processes and strategies considering a seamless fusion of multiple data sources, as well as a mixed vehicle stream (autonomous, connected, and conventional vehicles) under real-world conditions of uncertain and missing data. Since trajectories for connected and conventional vehicles cannot be optimized or guaranteed, the project examines the impacts of the presence of automated vehicles on the following vehicles in a queue. The project also integrates advanced sensing technology needed to control a mixed vehicle stream, as well as address malfunctioning communications in connected and autonomous vehicles.

In the recent past the term "Smart Cities" was introduced to mainly characterize the integration into our daily lives of the latest advancements in technology and information. Although there is no standardized definition of Smart Cities, what is certain is that it touches upon many different domains that affect a city's physical and social capital. Smart cities are intertwined with traffic control systems that use advanced infrastructures to mitigate congestion and improve safety. Traffic control management strategies have been largely focused on improving vehicular traffic flows on highways and freeways but arterials have not been used properly and pedestrians are mostly ignored. This work proposes to introduce a novel hierarchical adaptive controls paradigm to urban network traffic control that will adapt to changing movement and interaction behaviors from multiple entities (vehicles, public transport modes, bicyclists, and pedestrians). Such a paradigm will leverage several key ideas of cyber-physical systems to rapidly and automatically pin-point and respond to urban arterial congestion thereby improving travel time and reliability for all modes. Safety will also be improved since advanced warnings actuated by the proposed cyber-physical system will alert drivers to congested areas thereby allowing them to avoid these areas, or to adapt their driving habits. Such findings have a tangible effect on the well-being, productivity, and health of the traveling public. The primary goal is to create a Cyber-Control Network (CCN) that will integrate seamlessly across heterogeneous sensory data in order to create effective control schemes and actuation sequences. Accordingly, this project introduces a Cyber-Physical architecture that will then integrate: (i) a sub-network of heterogeneous sensors, (ii) a decision control substrate, and (iii) a sub-actuation network that carries out the decisions of the control substrate (traffic control signals, changeable message signs). This is a major departure from more prevalent centralized Supervisory Control And Data Acquisition (SCADA), in that the CCN will use a hierarchical architecture that will dynamically instantiate the sub-networks together to respond rapidly to changing cyber-physical interactions. Such an approach allows the cyber-physical system to adapt in real-time to salient traffic events occurring at different scales of time and space. The work will consequently introduce a ControlWare module to realize such dynamic sub-network reconfiguration and provide decision signal outputs to the actuation network. A secondary, complementary goal is to develop a heterogeneous sensor network to reliably and accurately monitor and identify salient arterial traffic events. Other impacts of the project include the integration of the activities with practitioners (e.g., traffic engineers), annual workshops/tutorials, and outreach to K-12 institutions.

Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals. This cyber-physical system consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of the investigation will create a new paradigm for managing parking to reduce traffic congestion, emissions and fuel consumption and to enhance system resilience. These results will be disseminated broadly through publications, workshops and seminars. The research will provide interdisciplinary training to both graduate and undergraduate students. The results of this research also fills a void in our graduate transportation curriculum in which parking management gets little coverage. The investigators will organize an online short training course in Coursera and National Highway Institute to bring results to a broader audience. The investigators will also collaborate with Carnegie Museum of Natural History to develop an online digital map and related educational programs, which will be presented in the museum galleries during public events. Technically, new theories, algorithms and systems for efficient management of transportation infrastructure through parking will be developed in this research, leveraging cutting-edge sensing technology, communication technology, big data analytics and feedback control. The research probes massive individualized and infrastructure based traffic and parking data to gain a deeper understanding of travel and parking behavior, and develops a novel reservoir-based network flow model that lays the foundation for modeling the complex interactions between parking and traffic flow in large-scale transportation networks. The theory will be investigated at different levels of granularity to reveal how parking information and pricing mechanisms affect network flow in a competitive market of private and public parking. In addition, this research proposes closed-loop control mechanisms to enhance mobility and sustainability of urban networks. Prices, access and information of publicly owned on-street and off-street parking are dynamically controlled to: a) change day-to-day behavior of all commuters through day-to-day travel experience and/or online information systems; b) change travel behavior of a fraction of adaptive travelers on the fly who are aware of time-of-day parking information and comply to the recommendations; and c) influence the market prices of privately owned parking areas through a competitive parking market.

Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals. This cyber-physical system consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of the investigation will create a new paradigm for managing parking to reduce traffic congestion, emissions and fuel consumption and to enhance system resilience. These results will be disseminated broadly through publications, workshops and seminars. The research will provide interdisciplinary training to both graduate and undergraduate students. The results of this research also fills a void in our graduate transportation curriculum in which parking management gets little coverage. The investigators will organize an online short training course in Coursera and National Highway Institute to bring results to a broader audience. The investigators will also collaborate with Carnegie Museum of Natural History to develop an online digital map and related educational programs, which will be presented in the museum galleries during public events. Technically, new theories, algorithms and systems for efficient management of transportation infrastructure through parking will be developed in this research, leveraging cutting-edge sensing technology, communication technology, big data analytics and feedback control. The research probes massive individualized and infrastructure based traffic and parking data to gain a deeper understanding of travel and parking behavior, and develops a novel reservoir-based network flow model that lays the foundation for modeling the complex interactions between parking and traffic flow in large-scale transportation networks. The theory will be investigated at different levels of granularity to reveal how parking information and pricing mechanisms affect network flow in a competitive market of private and public parking. In addition, this research proposes closed-loop control mechanisms to enhance mobility and sustainability of urban networks. Prices, access and information of publicly owned on-street and off-street parking are dynamically controlled to: a) change day-to-day behavior of all commuters through day-to-day travel experience and/or online information systems; b) change travel behavior of a fraction of adaptive travelers on the fly who are aware of time-of-day parking information and comply to the recommendations; and c) influence the market prices of privately owned parking areas through a competitive parking market.