Rapid Transit Systems such as metro or bus lines constitute prime targets for terrorism and criminal activity. The key reason for this is the large number of passengers that systems such as the LA Metro or the Chicago transit system carry every day (e.g. approximately 350,000 people for the LA Metro and 750,000 for the Chicago transit system).
Hence, the intelligent deployment of human resources such as officers and canine units to patrol such systems constitutes a key challenge for several security agencies. In particular, avoiding predictable schedules is extremely important. Any predictable pattern could be exploited by criminals or terrorists to attack a bus or metro line. To avoid this, we developed TRUSTS a decision-aid tool to produce accurate randomized patrol schedules for each patrol officer. TRUSTS is composed of two main components: (i) a main system which computes patrol schedules and a mobile app that each officer can use to request a patrol schedule or to submit information about the checks that he made during his shift. In what follows, we discuss each component in detail.
Scheduling Randomized Patrols for Fare Evasion Deterrence in Transit System
TRUSTS has been designed to produce effective patrol schedules to deter fare evasion within the Los Angeles Metro system (see the Figure). TRUSTS models the patrolling problem as a Bayesian Stackelberg game where patrollers act as defenders and fare evaders as attackers of the system. Thus far, our research has investigated ways to produce accurate, scalable and robust approaches that could adapt and handle the uncertainty and the unpredictability of a transit system. The results of this research were published in a number of papers at several international conferences. We refer the interested reader to the list down below to download the papers. Currently, we are investigating ways to extend our system to incorporate crime prevention and counter-terrorism.
(This content is being transfered from http://teamcore.usc.edu/projects/TRUSTS/default.html)