Learning and Control for Accessible, Safe, and Equitable Transportation

Organizers: C. Cassandras, A.A. Malikopoulos, C. Wu

Connected and automated vehicles (CAVs) are expected to gradually penetrate the market in ways that will improve safety and transportation efficiency over the next several years. While the technological benefits of CAVs are clear, their societal impact has not yet been adequately studied. Thus, in this session, we aim to report on recent research developments involving learning and control methodologies that capture this impact and provide solutions that (1) mitigate any potential rebound effects, e.g., increased vehicle miles traveled, increased travel demand, and empty vehicle trips; (2) address fundamental issues of accessibility, safety, and equity in transportation in terms of fairly and safely managing access to the system resources at several different levels and to several different classes of users (CAVs, human-driven vehicles, pedestrians, bikers) in a mixed traffic setting. We anticipate that efficient transportation and travel cost reduction might alter human travel behavior causing rebound effects, e.g., by improving efficiency, travel cost is decreased; hence, willingness to travel is increased. The latter would increase overall vehicle miles traveled, which in turn might negate the benefits in terms of energy and travel time. Moreover, we need to ensure that all travelers in a mixed traffic setting are treated equitably in terms of access and sharing of system resources at several different levels, e.g., routing options and modes of transportation have similar performances in terms of travel time and cost.