An Offline-Online Integration Approach for Security Traffic Patrolling With Frequency Constraints

Due to the increasing need to protect public security, this article studies the security traffic patrolling (STP) problem, where a collection of police officers plan to patrol around a city. In STP, the patrolling policy should not only take drivers' opportunistic behaviors into consideration but also satisfy frequency constraints such that hot-spot regions are patrolled at least once every several periods. Existing randomized methods are efficient in reducing traffic law violations but can only satisfy the frequency constraints in a probabilistic manner. Traditional planning methods can be employed to meet the frequency constraints in a deterministic manner. However, it is difficult to find the deterministic patrolling paths for city-scale STP with hundreds of police officers and regions. Against this background, this article proposes a novel two-stage offline-online integration framework to guarantee frequency constraints while efficiently preventing traffic law violations of drivers. In the offline stage, a linear programming (LP)-based randomized policy is designed, where the patrolling efficiency is modeled as the objective and the frequency constraint is modeled in a probabilistic manner. Guided by the offline policy, in the online stage, by observing the real distribution of police officers, real-time planning is proposed to reschedule the police to guarantee the frequency constraints. Extensive empirical experiments on synthetic and real datasets are conducted to validate the proposed framework. The results demonstrate that compared with existing baseline solutions, the proposed two-stage STP framework can reduce the driver violation rate as much as possible, satisfy frequency constraints and scale well to STP in a real-time fashion.