Game Theoretic Patrol Strategies for Transit Systems the Trusts System and Its Mobile App (original) (raw)
Related papers
TRUSTS: Scheduling Randomized Patrols for Fare Inspection in Transit Systems Using Game Theory
AI Magazine, 2012
In proof-of-payment transit systems, passengers are legally required to purchase tickets before entering but are not physically forced to do so. Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. The deterrence of fare evasion depends on the unpredictability and effectiveness of the patrols. In this paper, we present TRUSTS, an application for scheduling randomized patrols for fare inspection in transit systems. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the objective is to deter fare evasion and hence maximize revenue. This problem differs from previously studied Stackelberg settings in that the leader strategies must satisfy massive temporal and spatial constraints; moreover, unlike in these counterterrorism-motivated Stackelberg applications, a large fraction of the ridership might realistically consider fare evasion, and so the number of followers is potentially huge. A third key novelty in our work is deliberate simplification of leader strategies to make patrols easier to be executed. We present an efficient algorithm for computing such patrol strategies and present experimental results using real-world ridership data from the Los Angeles Metro Rail system. The Los Angeles County Sheriff’s department is currently carrying out trials of TRUSTS.
TRUSTS: Scheduling Randomized Patrols for Fare Inspection in Transit Systems
In proof-of-payment transit systems, passengers are legally required to purchase tickets before entering but are not physically forced to do so. Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. The deterrence of such fines depends on the unpredictability and effectiveness of the patrols. In this paper, we present TRUSTS, an application for scheduling randomized patrols for fare inspection in transit systems. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the ob- jective is to deter fare evasion and hence maximize revenue. This problem differs from previously studied Stackel- berg settings in that the leader strategies must satisfy massive temporal and spatial constraints; moreover, unlike in these counterterrorism-motivated Stackelberg applications, a large fraction of the ridership might realistically consider fare evasion, and so the number of followers is potentially huge. A third key novelty in our work is deliberate simplification of leader strategies to make patrols easier to be executed. We present an efficient algorithm for computing such patrol strategies and present experimental results using real-world ridership data from the Los Angeles Metro Rail system. The Los Angeles Sheriff’s department has begun trials of TRUSTS.
Security Games in the Field: Deployments on a Transit System
2014
This paper proposes the Multi-Operation Patrol Scheduling System (MOPSS), a new system to generate patrols for transit system. MOPSS is based on five contributions. First, MOPSS is the first system to use three fundamentally different adversary models for the threats of fare evasion, terrorism and crime, generating three significantly different types of patrol schedule. Second, to handle uncertain interruptions in the execution of patrol schedules, MOPSS uses Markov decision processes (MDPs) in its scheduling. Third, MOPSS is the first system to account for joint activities between multiple resources, by employing the well known SMART security game model that tackles coordination between defender’s resources. Fourth, we are also the first to deploy a new Opportunistic Security Game model, where the adversary, a criminal, makes opportunistic decisions on when and where to commit crimes. Our fifth, and most important, contribution is the evaluation of MOPSS via real-world deployments, providing data from security games in the field.
Scalable Randomized Patrolling for Securing Rapid Transit Networks
2013
Mass Rapid Transit using rail is a popular mode of transport employed by millions of people in many urban cities across the world. Typically, these networks are massive, used by many and thus, can be a soft target for criminals. In this paper, we consider the problem of scheduling randomised patrols for improving security of such rail networks. Similar to existing work in randomised patrols for protecting critical infrastructure, we also employ Stackelberg Games to represent the problem. In solving the Stackelberg games for massive rail networks, we make two key contributions. Firstly, we provide an approach called RaPtoR for computing randomized strategies in patrol teams, which guarantees (i) Strong Stackelberg equilibrium (SSE); and (ii) Optimality in terms of distance traveled by the patrol teams for specific constraints on schedules. Secondly, we demonstrate RaPtoR on a real world data set corresponding to the rail network in Singapore. Furthermore, we also show that the algorithm scales easily to large rail networks while providing SSE randomized strategies.
Attacker-Defender Stackelberg security games (SSGs) have emerged as an important research area in multi-agent systems. However, existing SSGs models yield fixed, static, schedules which fail in dynamic domains where defenders face execution uncertainty, i.e., in domains where defenders may face unanticipated disruptions of their schedules. A concrete example is an application involving checking fares on trains, where a defender's schedule is frequently interrupted by fare evaders, making static schedules useless.
Towards optimal patrol strategies for fare inspection in transit systems
2012
In some urban transit systems, passengers are legally required to purchase tickets before entering but are not physically forced to do so. Instead, patrol units move about through the transit system, inspecting tickets of passengers, who face fines for fare evasion. This setting yields the problem of computing optimal patrol strategies satisfying certain temporal and spacial constraints, to deter fare evasion and hence maximize revenue. In this paper we propose an initial model of this problem as a leader-follower Stackelberg game. We then formulate an LP relaxation of this problem and present initial experimental results using real-world ridership data from the Los Angeles Metro Rail system.
Robust Dynamic Distribution of Security Assets in Transit Systems
Transportation Research Record: Journal of the Transportation Research Board, 2013
A robust, mixed-integer, multi-stage program is presented that seeks to effectively secure a transit system where risk is considered to be dynamic and varies over time. A time-varying risk measure reflects the unique nature of transit systems, where accumulation of passengers at transfer facilities, stations and transit vehicles is dynamic and increases the vulnerability of transit users and system to adverse events. The model is robust under uncertainty and better matches security assets at stations in the face of timevarying risk by redistributing them. The volume-dependent risk measure and subsequent deployment of security assets are developed for the transit system in Washington, D.C. demonstrating the variable nature of risk and response. The value of considering a robust solution is demonstrated by comparing the robust approach to an expected value approach. Five scenarios, designed on recent events on the system, replicate the operational conditions of the transit system for the morning rush hour period and show the effectiveness of the developed deployment strategies.
Using Game Theory for Los Angeles Airport Security
Ai Magazine, 2009
Security at major locations of economic or political impor- tance is a key concern around the world, particularly given the threat of terrorism. Limited security resources prevent full security coverage at all times, which allows adversaries to observe and exploit patterns in selective patrolling or mon- itoring, e.g. they can plan an attack avoiding existing pa- trols. Hence, randomized patrolling
Journal of Public Transportation, 2005
The events of September 11th, 2001, brought the issue of transportation security and terrorism to the forefront of civil society. Transit security is especially challenging because of the nature of transit systems as open and accessible public places and the need to keep these systems running quickly and efficiently; transit officials cannot employ many of the security strategies used in aviation security. This paper examines the recent developments in transit security planning in the U.S. using two sources of data: 1) interviews with officials from federal agencies, a national transit industry organization, and local transit agencies, and 2) a nationwide survey of transit operators. The findings show that transit security remains a major concern for operators who must work to balance security needs with operations and management goals. Interagency coordination has become a crucial element of security planning. In addition, environmental design and public outreach and education-two strategies that received much less attention pre-September 11th-have emerged as much more important in transit security planning. at Chapel Hill, and before that a Transportation Analyst with the Metropolitan Transportation Commission in Oakland, California.
Strategic Security Placement in Network Domains with Applications to Transit Security
2009
Deterministic placement of security personnel creates serious vulnerabilities for any organization attempting to prevent intrusion. Recent work in use at the Los Angeles International Airport (LAX) and in progress with the United States Federal Air Marshal Service (FAMS) has applied game-theoretic analysis to the problem by modeling it as a Stackelberg game wherein security forces are the leaders that commit to a strategy that is observed and countered by attackers. In this work, we explore efficient techniques for performing the same analysis on games with a graph structure, wherein an attacker must follow a path from an entry point to a target. If we frame these problems in the straightforward manner with leader actions being sets of edges that can be guarded and follower actions being paths from entry to targets, the size of the game increases exponentially, quickly reaching memory limitations when using general Stackelberg solvers. In this work, we propose a novel linear program that is able to solve this type of problem efficiently. While it provides exact solutions for games where only one checkpoint is allowed, it is an approximation in the general case. Finally, we compare the performance of this and other methods by generating optimal policies for the Seoul Metropolitan Subway in Seoul, South Korea.