TRUSTS: Scheduling Randomized Patrols for Fare Inspection in Transit Systems Using Game Theory

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. 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 execute. 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. There are, quite literally, no barriers to entry, as illustrated in figure 1. Instead, security personnel are dynamically deployed throughout the transit system, randomly inspecting passenger tickets. This proof-of-payment fare collection method is typically chosen as a more cost-effective alternative to direct fare collection, that is, when the revenue lost to fare evasion is believed to be less than what it would cost to make fare evasion impossible. For the LA Metro, with approximately 300,000 riders daily, this revenue loss can be significant; the annual cost has been estimated at $5.6 million. The Los Angeles Sheriff's Department (LASD) deploys uniformed patrols onboard trains and at stations for fare checking (and for other purposes such as crime prevention), in order to discourage fare evasion.