Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

A best-case Rosenthal equilibrium based coordination mechanism for N-person online routing games of connected and automated vehicles

In this paper, the authors propose a best-case Rosenthal equilibrium based coordination mechanism for online automated routing decisions of connected and automated vehicles (CAVs) in a connected transportation network system. The objective of the coorination mechanism is to coordinate between the multiple equilibria in an n-person online routing game so that the authors can make better route choice decisions for CAVs. The mechanism is modeled as a pure-strategy congestion game. The model assumes that individual CAVs are non-cooperative and try to minimize their own travel time in the network. The proposed mechanism tries to coordinate between these equilibria considering both total travel time and individual payoff. Based on the original Rosenthal’s integer programming formulation of a congestion game, the authors propose an integer linear programming formulation that can be directly solved by a solver in a column generation based solution framework. Numerical experiments using Sioux Falls network substantiate the existence of multiple equilibria and validate that the solution derived from the proposed algorithm is a Nash equilibrium. The convergence of the algorithm is also consolidated from the experiment results