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Published at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013).
An expensive and prevalent problem worldwide, fare evasion in proof-of-payment transit systems introduces a need for randomized patrol strategies that effectively deter fare evasion and maximize transit system revenue. The Tactical Randomizations for Urban Security in Transit Systems (TRUSTS) approach addresses this challenge by using Bayesian Stackelberg games to model the transit patrolling problem and efficiently solving for the optimized patrol strategy for each patrol officer shift. In order to implement the TRUSTS approach in real-world transit systems, the METRO mobile app presented in this paper is being developed to work with TRUSTS to (i) provide officers with real-time TRUSTS-generated patrol schedules, (ii) provide recovery from unexpected schedule interruptions that can occur in real-world patrolling domains, and (iii) collect valuable patrol data for system analysis. An innovation in transit system patrolling technology, the METRO mobile app is an online agent that interacts with the user as an interface between the patrol officer and TRUSTS. In this paper, we propose a demonstration of the TRUSTS system, composed of the TRUSTS and METRO app components, focusing on the mobile app for user interaction. Providing a brief overview of the problem setting being addressed and the system components, this demonstration showcases how the TRUSTS system works and enables successful and robust deployment in the Los Angeles Metro System.
Demo video: http://www.youtube.com/embed/_lUG08ODqTI