This project will develop artificial intelligence techniques to improve the operation of large-scale infrastructure systems, such as smart transportation networks and electric power grids, which are essential to modern life. These complex systems, known as societal-scale cyber-physical systems, integrate physical infrastructure with thousands of sensors, computing devices, and actuators. Due to their scale and distributed nature, managing these systems in real time poses a significant challenge. To address this challenge, the project will leverage deep reinforcement learning, a form of artificial intelligence that learns optimal decision-making strategies directly from data. By improving the efficiency and reliability of critical infrastructure systems, the research will further the national interest through reducing traffic congestion, improving emergency response times, and increasing the stability of the power grid.

The AI-Powered Autonomy-Aware Neighborhood Mobility Zones project reimagines public transportation for mid-sized U.S. cities through an AI-driven, multi-modal mobility framework that integrates fixed-line buses and on-demand microtransit into a single, responsive system. Led by the Chattanooga Area Regional Transportation Authority (CARTA) with technical contribution from the Pennsylvania State University, Vanderbilt University, and Cornell University, this $7 million initiative is designed to enhance infrastructure utilization, mobility access, and financial viability of public transit in Chattanooga while providing a national model for AI-enabled mobility resilience.

This Civic Innovation Challenge (CIVIC) Stage 1 project will perform research to address transportation challenges faced by residents and workers in rural areas around industrial hubs. Rapid economic growth in these areas demands efficient public transit systems that can serve a geographically dispersed workforce with strict arrival times. However, existing solutions often struggle with these unique requirements, leading to traffic congestion, pollution, and limited access to essential services for residents. This research project will develop and deploy a novel multi-modal transit system for Blue Oval City (BOC), a new rural industrial hub in Stanton, Tennessee. The system will combine fixed-line buses with on-demand micro-transit services, addressing the challenges posed by the geographically dispersed workforce and strict arrival times.

Food insecurity is the lack of consistent and reliable access to nutritious food. In the Houston (Texas) area, over 14% of Harris County households experienced food insecurity before the emergence of COVID-19. It is unclear how the nutritional needs of Houston’s vulnerable populations will be served when the next devastating weather event strikes the region, given that the city is already experiencing multiple disasters, including COVID-19, economic disruptions, and systemic food insecurity. The Houston Food Bank (HFB) collaborates with over 1,500 partners to address the needs of families experiencing food insecurity. Through a community-driven approach, this project brings together civic collaborators with university researchers to align HFB’s food distribution strategy to match food insecurity during multiple disaster profiles. Our project will develop indicators of individual and community equity for food distribution during pandemics and extreme weather events; design a network organizational resilience index for food bank networks and interventions to improve network resilience; develop and validate a predictive tool for infrastructure vulnerability, and develop and validate an artificial-intelligence based decision-making tool for determining the locations of food distribution hubs and their food allocation.

This Houston-based project establishes a collaborative process with community and commercial technology partners to accelerate the equitable development of accessible fast charging infrastructure and electric vehicle (EV) ownership for low income families by leveraging regional markets of early EV adopters. The novelty of the project lies in a community-driven participatory approach that integrates both social and technical dimensions, bringing transformational change to EV ownership and electrification of smart public transportation. This will be achieved using a data-driven model that integrates real-time data from micro-transit, fixed-route transit, and carpooling services to predict and overcome the uncertainties of traffic conditions, which would result in uncertain travel times and poor reliability.

In every public transit system, a trade-off has to be made between concentrating service into very useful routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service. Improving the efficiency of an existing system while enhancing service in terms of both usefulness and coverage presents considerable challenges. These challenges to operational efficiency are exacerbated by the requirement to provide complementary paratransit services, which are typically characterized by very low efficiency (energy per passenger per mile) and attendant high cost of operation. Our vision is to address these challenges by combining the complementary advantages of fixed- and dynamic-route transit services and seamlessly integrating them. We focus on the following objectives: minimizing energy used per passenger per mile, minimizing passenger wait and trip times, maximizing service coverage, and maximizing the percentage of daily trips serviced by transit. To explore this complex decision space, we will design, implement, and evaluate an artificial intelligence engine, which will enable agencies with mixed-vehicle fleets (EVs, ICEVs, etc.) to operate integrated fixed-dynamic transit services that maximize energy efficiency and make transit more accessible.

Public transportation infrastructure is an essential component in cultivating equitable communities. However, public transit agencies have historically struggled to achieve this since they are often severely stressed in terms of resources as they have to make the trade-off between concentrating service into routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service. A solution that holds great promise for improving public transit systems is the integration of fixed-route services with microtransit systems: multi-passenger transportation services that serve passengers using dynamically generated routes and may expect passengers to make their way to and from common pick-up or drop-off points. However, most microtransit systems have failed in the past due to the lack of community engagement, inability to handle the uncertainty of operations when integrating the fixed transit, and inability to handle the system-level optimization challenges.

The COVID-19 pandemic has not only disrupted the lives of millions but also created exigent operational and scheduling challenges for public transit agencies. Agencies are struggling to maintain transit accessibility with reduced resources, changing ridership patterns, vehicle capacity constraints due to social distancing, and reduced services due to driver unavailability. A number of transit agencies have also begun to help the local food banks deliver food to shelters, which further strains the available resources if not planned optimally. At the same time, the lack of situational information is creating a challenge for riders who need to understand what seating is available on the vehicles to ensure sufficient distancing. In partnership with the transit agencies of Chattanooga, TN, and Nashville, TN, the proposed research will rapidly develop integrated transit operational optimization algorithms, which will provide proactive scheduling and allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (i.e., disinfection). A key component of the research is the design of privacy-preserving camera-based ridership detection methods that can help provide commuters with real-time information on available seats considering social-distancing constraints.

The goal of this project is to develop a high-resolution system-level data capture and analysis framework to revolutionize the operational planning of a regional transportation authority, specifically the Chattanooga Area Regional Transportation Authority (CARTA). There is existing research on improving energy efficiency in transportation networks through analyzing energy consumption data per vehicle type and driving context. However, these studies are based on trip specific estimation and thus cannot be applied to a regional transportation network. Further, a number of these studies are based on simplified model estimation that is used within a simulation framework for analysis and are therefore difficult to validate during actual driving/road conditions that are not captured in the training dataset (which is typically limited in size and features).