Project: #352 Evaluating Resilience in Mixed-Autonomy Transportation Systems Progress Report - Reporting Period Ending: March 31, 2021 Principal Investigator: Carlee Joe-Wong Status: Active Start Date: Jan. 1, 2021 End Date: June 30, 2022 Research Type: Applied Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: 2021 Mobility UTC "Big Idea" Progress Report (Last Updated: March 31, 2021, 12:52 p.m.) % Project Completed to Date: 17 % Grant Award Expended: 0 % Match Expended & Document: 0 USDOT Requirements Accomplishments The objective of this project is to evaluate how autonomous vehicles can improve the resilience of transportation networks to exogenous disturbances like road construction, traffic accidents, and congestion. We plan to use our analysis to develop guidelines for regulators to specify the behavior of autonomous vehicles in a road network, so as to maximize resilience. To accomplish this goal, we plan to complete three major tasks. First, we will formulate a model of cascading failures in road networks, which captures the effects of users redistributing themselves around a disturbance, which can then cause congestion on nearby roads, leading to a cascade of road congestion and potentially failure of the network overall. This model will include the behavior of autonomous and human-driven vehicles; they may react differently to disturbances in transportation networks. Second, we will validate our models with simulations on road traffic simulators that can capture the behavior of autonomous as well as human-driven vehicles. Finally, we will translate our findings into policies that regulators may be able to impose on autonomous vehicles as they are more widely deployed. Since the start of the project in January, we have made progress on our first task of formulating the cascading failure model for transportation networks. Specifically, we have considered the case of two networks that operate in parallel (for example, a road network and train network), where work can be offloaded or transferred between the two. Drivers, for example, may choose to take the train instead of driving if road conditions are bad. Prior work in this area has considered static offloading strategies, where a fixed percentage of users transfer from one network to another. Our work instead considers a dynamic strategy in which the proportion of users transferring may change depending on the current state of the network. We show that such a dynamic strategy outperforms static transfer strategies, in the sense that the network can survive larger initial failures when dynamic transfer strategies are followed. Our goal for the next six month reporting period is to continue to develop our cascading failure model. While our current work studies drivers transferring between two transportation networks, we plan to extend this work to also examine how drivers might reroute themselves around disturbances in a single network. We will also modify our current model to ensure that it can represent different redistribution actions for autonomous and human-driven vehicles. For example, autonomous vehicles may attempt to optimize their rerouting strategy around disturbances, and may be able to access more information on the optimal redistributions than human-driven vehicles would. We also plan to begin implementing our model in our existing traffic simulator, specifically ensuring that we can model different vehicle redistribution and routing strategies when disturbances occur. Impacts Our initial framework analyzes how users should transfer from one transportation network to another so as to avoid cascading failures in each network. This work can help network operators identify conditions under which their networks are in danger of failing, and suggest ways in which they can avert failures by diverting users to an alternative mode of transport. We are in the final stages of preparing a journal submission to IEEE Transactions on Network Science and Engineering based on this initial work. Other No other outputs for this reporting period. Outcomes New Partners None to report. Issues No changes to report.