Game-theoretic Patrol Strategies for Transit Systems (Slideshow deck)

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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.

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  • In this AAMAS(ahh-mus) 2013 demonstration video, we present the TRUSTS system, an interactive agent-based software system that generates randomized patrol strategies for transit systems.
  • Part 1 of the demonstration provides background context for the TRUSTS system and an overview of its two main components: TRUSTS and its mobile app. This demonstration focuses on the mobile app for user interaction.
  • First, we’ll introduce the problem our system addresses. In proof of payment transit systems, passengers are legally required to purchase a ticket before boarding a metro train or bus. However, resource limitations prevent patrol officers from actually verifying that every passenger has done so. Instead, patrol officers inspect a subset of passengers based on some patrol strategy. Violations and fines are issued to any fare evaders they catch.
  • Successful fare evaders, on the other hand, cost the transit system their ticket fare in revenue loss, accumulating to potentially significant amounts over time. In 2007 alone, the Los Angeles Metro system, where our demonstration simulation takes place, lost an estimated $5.6 million in revenue due to fare evasion [2].In order to address this costly issue, there is a need for effective patrol strategies that are unpredictable by passengers and that maximize transit system revenue revenue. Due to the complexity of this patrolling problem, human schedulers cannot manually generate these optimized patrol strategies.
  • Based on an approach successfully deployed in other security applications, such the police patrolling at LAX, TRUSTS uses Stackelberg games to model the L.A. metro system. In this model, the patrol officer, represented by the leader, commits to a patrol strategy and the potential fare evaders, represented by the followers, observe this patrol strategy and select a counter strategy accordingly [4].
  • Unique from the other applications, transit systems impose both temporal and spatial constraints on the domain model (shown here in the LA Metro Gold Line timetable and route map), making the model too complicated to be efficiently solved in this form.
  • The TRUSTS approach addresses this problem by reducing the constraints into a single transition graph, representing all possible patrol office action movements in the transit system as flows from each station node in the graph. In addition, TRUSTS introduces duplicate history station nodes into this graph to represent a state for the past action, allowing the current patrol schedule to be recovered if the patrol officer unexpectedly misses a patrol action.
  • Finally, TRUSTS extracts the maximum patrol strategy, or optimal flow through the transition graph shown here, using the Decomposed Optimal Bayesian Stackelberg Solver (DOBSS). This solution describes the optimized patrol action from each state node in the graph [4].
  • For robust deployment of TRUSTS in real-world transit systems, we have developed the METRO app, an innovation in transit system patrol scheduling. The METRO app is a software agent carried by each patrol officer that provides an interface for interaction between the user and TRUSTS. Shown in the this figure, The METRO app provides three principal features: a TRUSTS-generated patrol schedule for the current shift, a tracking system for reporting passenger violations, and a shift statistics summary report.The TRUSTS component runs on a server machine and uses the specified transit system parameters, including route schedules, shift start times, and number of patrol officers, to compute patrol strategies f or each patrol officer’s shift. These patrol strategies are stored in the TRUSTS database for retrieval by the METRO app. At the beginning of an officer’s shift, the METRO app queries the TRUSTS database for the user’s patrol strategy for the current shift. At the end of the shift, the officer submits the patrol and violations data captured throughout their shift. This information is stored in the same TRUSTS database, associated with its generated patrol strategy.
  • These are the three main views of the METRO app: Schedule View, Reporting View, and Summary View.
  • Schedule View allows the patrol officer to see their current patrol action as well as upcoming patrol actions. When the end time of the current action is reached, the current action is completed and the view is updated for the next action. As previously discussed, TRUSTS supports schedule interruption recovery. If an unexpected event causes the patrol officer to miss an action, the METRO app allows users to update their current location, triggering a reschedule for the corresponding station node in the TRUSTS-generated patrol strategy and a new schedule to be shown.
  • Reporting View allows the officer to enter violation data for the current patrol action. This violation data is shown below in the view and included in the information sent back to TRUSTS at the end of the officer’s shift.
  • Summary View shows a generated statistics report for the officer’s shift. This view also allows patrol officers to view and edit the violation data of past actions. Finally, at the end of their shift, patrol officers use the submit report button to submit their shift patrol data to the TRUSTS database.
  • The interactive simulation, the focus of the demonstration, showcases the TRUSTS system running over the course of a shortened shift of a patrol officer in the L.A. Metro System. The participants experience the real-world deployment of the TRUSTS system first-hand from the perspective of the patrol officer using the METRO app.
  • For the demonstration setup, we have a laptop functioning as the TRUSTS server that is connected to a monitor, displaying what the TRUSTS system is doing at the present state in the shift simulation. Throughout the shift simulation, the monitor prompts the user with various event occurrences and instructions on how to use the METRO app in response to these real-world scenarios.The laptop runs TRUSTS on an L.A. Metro Gold Line-based dataset, modified to support a two-minute patrol officer shift for the demonstration, to generate a patrol strategy for the simulated shift. The METRO app, deployed on the mobile phone shown here, has also been modified for the demonstration to continuously communicate the METRO app’s reporting data to the TRUSTS server for display on the monitor.
  • To conclude our demonstration, we now briefly touch on our expectations from deploying this system in the LA Metro.
  • The TRUSTS approach has already shown to be successful in increasing fare deterrence in LA Mero simulation and trial testing.By working with the LAPD to deploy our robust system in the real world, we expect to address their fare evasion problem with this novel application. In addition, through analysis on the METRO app-collected patrol data, we expect to gain valuable insight on the L.A. Metro System patrolling domain, such as follower behavior patterns, and be able to evaluate the effectiveness of TRUSTS system deployment in the transit system. This patrol data is also useful for transit systems that manually record violations data or perform their own analysis on this information.
  • Game-theoretic Patrol Strategies for Transit Systems (Slideshow deck)

    1. 1. THE TRUSTS SYSTEMTactical Randomizations for Urban Security inTransit Systems and its Mobile App Submission 25
    2. 2. THE TRUSTS SYSTEMPart 1: An Overview of the TRUSTS System Submission 25
    3. 3. Setting: Proof of Payment Transit Systems
    4. 4. Motivation: Fare Evasion in L.A. Metro
    5. 5. The Stackelberg Games Model
    6. 6. Modeling the Transit Patrolling Problem
    7. 7. The TRUSTS Approach Reduced Transit System Transition Graph
    8. 8. TRUSTS-Generated Schedules Optimized Patrol Strategy
    9. 9. TRUSTS and The METRO App
    10. 10. The METRO AppSchedule View Reporting View Summary View
    11. 11. The METRO App: Schedule View
    12. 12. The METRO App: Reporting View
    13. 13. The METRO App: Summary View
    14. 14. THE TRUSTS SYSTEMPart 2: Interactive Simulation Submission 25
    15. 15. Setup Simulation Display MonitorTRUSTS The METRO App
    16. 16. Start of the Shift • Launch the app into the Schedule View to see the TRUSTS- generated patrol schedule
    17. 17. Current Patrol Action: Station Check
    18. 18. Patrol Event Occurrence • No violations found!
    19. 19. Patrol Officer Response • Prepare for next patrol action
    20. 20. Current Patrol Action: Metro Check
    21. 21. Patrol Event Occurrence • Four inspected passengers have valid tickets
    22. 22. Patrol Officer Response • Use Reporting View to report four passed passenger checks in this shift segment
    23. 23. Patrol Event Occurrence • Fare evader caught!
    24. 24. Patrol Officer Response • Use Reporting View to report one violation issued in this shift segment • Prepare for next patrol action
    25. 25. Current Patrol Action: Station Check
    26. 26. Patrol Event Occurrence • An arrest was made!
    27. 27. Patrol Officer Response • Use Reporting View to report one arrest in this shift segment
    28. 28. Patrol Event Occurrence • The arrest caused the patrol officer to miss the next patrol action!
    29. 29. Patrol Event Response • Update the location to the current station
    30. 30. Patrol Event Response • Select the current station
    31. 31. Metro App Response • Current location is updated • A new schedule is generated from the TRUSTS-generated strategy • The officer can continue their shift with the new schedule
    32. 32. End of the Shift • The officer submits their shift data report
    33. 33. THE TRUSTS SYSTEMPart 3: Real-World System Deployment Submission 25
    34. 34. Robust Deployment in the L.A. Metro System• Successful in L.A. Metro System simulation and trial testing• Deter fare evasion and crime• Maximize transit system revenue• Valuable data collection for analysis by TRUSTS research team and L.A. Police Department• Efficient violation data reporting for LAPD officers
    35. 35. THE TRUSTS SYSTEM Thank you! Submission 25

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