@conference {1503628,
	title = {Game-theoretic Patrol Strategies for Transit Systems: the TRUSTS System and its Mobile App (Demonstration) },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)[Demonstrations Track]},
	year = {2013},
	abstract = {Fare evasion costs proof-of-payment transit systems significant
losses in revenue. In 2007 alone, the Los Angeles Metro system,
using proof-of-payment, suffered an estimated revenue loss of
$5.6 million due to fare evasion [2]. In addition, resource
limitations prevent officers from verifying all passengers. Thus,
such officers periodically inspect a subset of the passengers based
on a patrol strategy. Effective patrol strategies are then needed to
deter fare evasion and maximize revenue in transit systems. In
addition, since potential fare evaders can exploit knowledge about
the patrol strategy to avoid inspection, an unpredictable patrol
strategy is needed for effectiveness. Furthermore, due to transit
system complexity, human schedulers cannot manually produce
randomized patrol strategies, while taking into account all of the
system{\textquoteright}s scheduling constraints [3].
In previous work on computing game-theoretic patrol strategies,
Bayesian Stackelberg games have been successfully used to
model the patrolling problem. In this model, the security officer
commits to a patrol strategy and the fare evaders observe this
patrol strategy and select a counter strategy accordingly [4]. This
approach has also been successfully deployed in real-world
applications, including by the L.A. International Airport police,
the U.S. Coast Guard at the Port of Boston, and the Federal Air
Marshal Service [5]. However, this approach cannot be used
within our setting due to the increased complexity of having more
potential followers and scheduling constraints [6]. In addition,
transit systems face the challenge of execution uncertainty, in
which unexpected events cause patrol officers to fall off schedule
and exist in unknown states in the model [1].
Addressing the increased complexity challenge, TRUSTS
(Tactical Randomizations for Urban Security in Transit Systems)
reduces the temporal and spatial scheduling constraints imposed
by the transit system into a single transition graph, a compact
representation of all possible movement throughout the transit
system as flows from each station node [1]. In addition, TRUSTS
remedies the execution uncertainty challenge by modeling the execution of patrol units as Markov Decision Processes (MDPs)
[1]. In simulation and trial testing, the TRUSTS approach has
generated effective patrol strategies for L.A. Metro System [1, 6].
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 realtime TRUSTS-generated patrol schedules, (ii) provide recovery
from schedule interruptions, and (iii) collect patrol data. An
innovation in transit system patrol scheduling technology, the app
works as an online agent that provides officers with the best set of
patrol actions for maximizing fare evasion deterrence based on the
current time and officer location. In this paper, we propose a
demonstration of the TRUSTS system, composed of the TRUSTS
and METRO app components, which showcases how the system
works with emphasis on the mobile app for user interaction. To
establish sufficient background context for the demonstration, this
paper also presents a brief overview of the TRUSTS system,
including the TRUSTS approach to patrol strategy generation in
Section 2.1 and discussion of the METRO app{\textquoteright}s features and user
interface design in Section 2.2, and the expected benefits from
deployment in the L.A. Metro System.},
	author = {Luber, Samantha and Yin, Zhengyu and Delle Fave, Francesco and Xin Jiang, Albert and Tambe, Milind and John P. Sullivan}
}
