Addressing Transit Accessibility and Public Health Challenges due to COVID-19
Lead PI:
Abhishek Dubey

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 datasets and algorithms developed through this program will be swiftly released to the research community in order to encourage a wider collaborative effort that will help other transit agencies that face similar challenges.

The intellectual merit of the proposed research lies in the design and evaluation of integrated operational optimization for both fixed-line and on-demand transit (including paratransit) under atypical capacity constraints, which requires maximizing transit access but minimizing contact. The challenge for optimization is the uncertainties that arise due to the atypical travel time and travel demand distribution, both of which need to be learned online again due to the changed scenarios. While it is possible to optimize these transit modes separately as prior work has done, integrated optimization can lead to significantly better results. However, this is difficult as the solution space of these problems is very large. The approach is based on rapidly composing and comparing the effectiveness of principled decision-theoretic approaches such as Monte Carlo tree search, optimal trip assignments using integer programming and problem-specific heuristics, and demand aggregation for on-demand transit. To develop a model for varying travel demand, the research uses novel neural network architectures to estimate usage and seating patterns in real-time from cameras that are already installed within transit vehicles. This will enable transit agencies to obtain travel demand even when they are running fare-free operations to minimize contact with drivers. Working with partner transit agencies, the researchers will be able to make the services more accessible for the community during these challenging times. This project directly relates to Smart and Connected Communities program as it demonstrates the importance of integration of technical and social research with strong community engagement in improving resilience of transit systems due to pandemics and other crises.

Abhishek Dubey
Prof. Dubey’s research interests are in the field of artificial intelligence and distributed computing for cyber-physical systems, and smart and connected communities. The fundamental contribution of his work lies in the co-design of resilient computational abstractions and the online learning algorithms and decision procedures for cyber-physical systems. He directs the SCOPE lab (Smart and resilient Computing for Physical Environment) at the Institute for Software Integrated Systems. The impact of his work can be seen with his partnerships in building transformation systems for the Nashville fire department, Nashville transit agency, Chattanooga Transit Agency, and the Tennessee Department of Transportation. Some of his key research results include the design of hierarchical decision procedures for responding to motor vehicle crashes, the design of energy-efficient transit operation procedures, and the design of transactive energy systems. Some of his recent publications can be obtained from his lab’s publication page. His work has been funded by NSF, NASA, DOE, ARPA-E. AFRL, DARPA, Siemens, Cisco, and IBM. Abhishek completed his Ph.D. in Electrical Engineering from Vanderbilt University in 2009. He received his M.S. in Electrical Engineering from Vanderbilt University in August 2005 and completed his undergraduate studies in Electrical Engineering from the Indian Institute of Technology, Banaras Hindu University, India in May 2001.
Performance Period: 06/01/2020 - 12/31/2021
Institution: Vanderbilt University
Sponsor: National Science Foundation
Award Number: 2029950