Patrol Routes Research Articles

The Programming Model of Air-Ground Cooperative Patrol Between Multi-UAV and Police Car

This paper investigates a patrol problem based on air-ground cooperation between multiple UAVs and police vehicles. Facing the uncertainty of patrol environment and patrol resources, the model guarantees the deterrence and emergency response capability of the patrol mission by optimizing the allocation strategy of patrol points and patrol routes. Relying on genetic algorithms, we encode patrol points and UAV launch/recovery points together to enhance the local search ability and convergence of the algorithm. Based on the real case of the D police station in Beijing, we explore the interactions among patrol elements and the impact on patrol tasks in different patrol environments. The results show that the Patrol missions formulated by Air-Ground Cooperative Patrol Optimization Model can be used to develop patrol tasks with better environmental adaptability. By analyzing the relationship between multiple groups of patrol elements, controlling the number of UAVs in future missions can improve the security of the area. And raise the ratio of hovering time in medium-risk areas to low-risk areas can improve the efficiency of patrols.

Mathematical home burglary model with stochastic long crime trips and patrolling: Applied to Mexico City

Mathematical models for predicting the geographical distribution of areas with high rates of home burglary and numerical experiments to evaluate the effectiveness of police patrol routing strategies in reducing crime can support security policymakers in planning more effective patrol routes. We give an overview of the model formulated by Jones et al. (2010) to study the effects of the presence of law enforcement on the formation of home-burglary hotspots. We propose an extension of the model to contemplate that a small proportion of burglars travel far away from their awareness space to burgle houses. We incorporate these trips in the form of long-range stochastic jumps that are biased towards more attractive sites. Simulations suggest that hotspots lose attractiveness as police more influences via deterrence on burglars’ decision making about returning home rather than burglarizing a house. The results indicate that a higher proportion of burglars making long crime journeys might counteract the deterrent effect form the police presence. A case study is carried out to predict at the zone and citywide levels the distribution of areas with the highest home burglary rates in Mexico City. The L-BFGS method is applied to get the values for the parameters in both the Jones et al. model and our model; we use data from home burglaries reported, patrol zones and prison admissions. The minimum of the objective function is slightly lower for our model than for the Jones et al. model. Results obtained through numerical simulations using our model better fit the spatial statistical distribution of home-burglary hotspots for the entire city than for the zones.

Cooperative Loitering Munition Swarm Online Patrolling Route Planning with Nonlinear Seeker Measurement

With the development of autonomous systems, the operational use of loitering munition is shifting from the following of a preplanned fixed route without communication to smart decision-making and collaborative cooperation with sharing information. In this paper, we study the autonomous decision-making and cooperative control strategy of online patrolling for a swarm of loitering munitions using communication to coordinate their route based on maximizing the information they gathered in the operation region. Taking the non-Gaussian nonlinear property of airborne radar seeker into account, we utilized a particle filter-based method to evaluate or to predict the information quality of each action candidate. We also implemented a coordinate descent scheme to enable a distributed and scalable swarm decision-making. Simulation results show that the proposed method provides a better estimation than baselines without the need for external or centralized decision agent.

Dynamic Guard Patrol in Stealth Games

Guard patrols are common in stealth games, both for a sense of realism and to form a basic challenge to acting out covert behaviour. Automated patrol route construction, however, is difficult, as simple approaches based on randomized movement or goal selection tend to result in unrealistic and overly repetitive guarding motion, and poor area coverage. In this work we introduce an approach based on modelling location “staleness” in a manner similar to occupancy maps, but applied to both a grid representation and a dynamically constructed coverage-visibility mesh. The latter provides a continuous abstraction of the game environment which can be efficiently used and maintained for real-time decision making. We use Unity3D and multiple game levels to compare our grid and mesh-based results, showing the advantages of the visibility mesh over the grid-based approach.

Exploratory Study on the Simulated Police Force Allocation of Shopping Mall Emergency Based on AnyLogic Platform

Due to the rapid development of society, public places, especially large shopping malls, are relatively frequent places for emergencies. Such emergencies not only seriously affect public security and property, but also bring great psychological pressure to citizens. Therefore, this study is of great significance for the exploration and study of public place emergencies.The research object of this study is the public shopping mall. Based on the AnyLogic simulation platform and guided by the relevant principles of social force model, this study utilize the pedestrian storehouse in the platform as the core module to build the simulation environment, and attempts to simulate the police force restraining effect on the overall event and the perpetrators after the occur of emergent incident under different police force allocations. In order to ensure the accuracy of the experimental data, the research team conducted field survey to estimate the average flow of people and the general data of the security personnel in shopping malls, also estimated the restrain and capture time after repeated experiments.The results indicate that increasing additional police force outside the shopping malls and pre-organizing reasonable patrol routes can obviously facilitate police officers to restrain perpetrators. Meanwhile, it is also clear that the Anylogic platform can effectively simulate pedestrian movement and interaction behaviour in emergencies.

A cost-beneficial area-partition-involved collaborative patrolling game in a large-scale chemical cluster

Terrorists often take the chemical clusters as the attacking target because of the adverse impacts of a chemical accident on society and the environment. In addition to some fixed countermeasures, previous studies have verified the feasibility of a patrol in addressing adversarial attacks. However, the previous patrolling practices fail to tackle the terrorist attacking problems in a large-scale area cost-effectively. To further tackle the protection issue with cost-beneficial solutions in a large-scale scenario, i.e., in a chemical cluster, we propose an area-partition-involved collaborative patrolling (APCP) game. We first leverage the proposed greedy deployment algorithm to determine the initial deployment of defenders (patrollers), including the quantity and position of patrol vehicles. Then, the large-scale area is partitioned into multiple smaller areas by using the collaborative idea of static partitioning. In the meantime, corresponding patrolling graphs are constructed based on graphic modeling methods. Finally, the APCP game is built between patrol vehicles (namely defender) and potential terrorists (namely attacker), in which patrol vehicles aim at detecting attack behaviors of terrorists by intelligently scheduling the patrolling routes. After formalizing the problem into a sequential game, we compute the Stackelberg equilibrium through the MultiLPs algorithm. Through case studies of three practical chemical cluster scenarios, the results explicitly show the superiority of our proposed APCP game by saving up to 25.48 % patrolling costs in a one-shot game compared to the results before partition. As for the collaborative patrolling problem in a large-scale area, the methods and models proposed in this paper can facilitate the management department of chemical clusters with intelligently scheduled patrolling routes, which can effectively reduce the cost of patrollers, and better protect the chemical cluster.

Realtime Predictive Patrolling and Routing with Mobility and Emergency Calls Data

A well-planned patrol route plays a crucial role in increasing public security. Most of the existing studies designed the patrol route in a static manner. Situations when rerouting of patrol path are required due to the emergencies, e.g., an accident or ongoing homicide, are not considered. In this paper, we formulate the crime patrol routing problem jointly with dynamic crime event prediction, utilising crowdsourced check-in and real-time emergency call data. The extensive experiment on real-world datasets verifies the effectiveness of the proposed dynamic crime patrol route using different evaluation metrics.

Analysing the Police Patrol Routing Problem: A Review

Police patrol is a complex process. While on patrol, police officers must balance many intersecting responsibilities. Most notably, police must proactively patrol and prevent offenders from committing crimes but must also reactively respond to real-time incidents. Efficient patrol strategies are crucial to manage scarce police resources and minimize emergency response times. The objective of this review paper is to discuss solution methods that can be used to solve the so-called police patrol routing problem (PPRP). The starting point of the review is the existing literature on the dynamic vehicle routing problem (DVRP). A keyword search resulted in 30 articles that focus on the DVRP with a link to police. Although the articles refer to policing, there is no specific focus on the PPRP; hence, there is a knowledge gap. A diversity of approaches is put forward ranging from more convenient solution methods such as a (hybrid) Genetic Algorithm (GA), linear programming and routing policies, to more complex Markov Decision Processes and Online Stochastic Combinatorial Optimization. Given the objectives, characteristics, advantages and limitations, the (hybrid) GA, routing policies and local search seem the most valuable solution methods for solving the PPRP.

Improvement of organizational measures to ensure public security and order during the mass events by the National Police of Ukraine

The purpose of this article is to identify ways to improve organizational measures to ensure public security and order during the mass events by the National Police. Methods such as structural-functional, formal-logical, modeling, analysis, and synthesis were used in the writing of the article. The successful implementation of this measure depends largely on organization, relationships between each police authority and unit, rational distribution of forces and resources, early response to the detection of violations and other events, effective coordination, placement of patrol posts and routes. The Department of Organizational Analytical Support and Rapid Response, which ensures within competencies its coordination, planning, control and coordination of actions of all authorities and units involved to ensure public security and order during mass events, plays an important role in the police work in this direction. It was therefore concluded that: a) improvement of decentralization of the management system of police authorities and units, establishment of the mechanism of its coordination with the territorial police bodies; b) equipping of police authorities and units with modern communication means, GPS-navigators, gas analyzers for identifying the state of drunkenness, video cameras, devices of operational identification, computing complexes etc.; c) developing close cooperation with citizens and their associations, with local communities; d) conducting trainings on keeping public order and providing public security, psychological and physical training, analytical work and more.

An Agent-Based Empirical Game Theory Approach for Airport Security Patrols

Airports are attractive targets for terrorists, as they are designed to accommodate and process large amounts of people, resulting in a high concentration of potential victims. A popular method to mitigate the risk of terrorist attacks is through security patrols, but resources are often limited. Game theory is commonly used as a methodology to find optimal patrol routes for security agents such that security risks are minimized. However, game-theoretic models suffer from payoff uncertainty and often rely solely on expert assessment to estimate game payoffs. Experts cannot incorporate all aspects of a terrorist attack in their assessment. For instance, attacker behavior, which contributes to the game payoff rewards, is hard to estimate precisely. To address this shortcoming, we proposed a novel empirical game theory approach in which payoffs are estimated using agent-based modeling. Using this approach, we simulated different attacker and defender strategies in an agent-based model to estimate game-theoretic payoffs, while a security game was used to find optimal security patrols. We performed a case study at a regional airport, and show that the optimal security patrol is non-deterministic and gives special emphasis to high-impact areas, such as the security checkpoint. The found security patrol routes are an improvement over previously found security strategies of the same case study.

Risk assessment for wildland fire aerial detection patrol route planning in Ontario, Canada

This study presents a model developed using a risk-based framework that is calibrated by experts, and provides a spatially explicit measure of need for aerial detection daily in Ontario, Canada. This framework accounts for potential fire occurrence, behaviour and impact as well as the likelihood of detection by the public. A three-step assessment process of risk, opportunity and tolerance is employed, and the results represent the risk of not searching a specified area for the detection of wildland fires. Subjective assessment of the relative importance of these factors was elicited from Ontario Ministry of Natural Resources and Forestry experts to develop an index that captures their behaviour when they plan aerial detection patrol routes. The model is implemented to automatically produce a province-wide, fine-scale risk index map each day. A retrospective analysis found a statistically significant association between points that aerial detection patrols passed over and their aerial detection demand index values: detection patrols were more likely to pass over areas where the index was higher.

A promising example of smart policing: A cross-national study of the effectiveness of a data-driven approach to crime and traffic safety

Smart policing emphasizes the combination of existing interdisciplinary datasets, improvement in analysis procedures, and design of more efficient policing strategies. One promising example, the Data-Driven Approach to Crime and Traffic Safety (DDACTS), integrates traffic crash and crime data into the design of more efficient patrol routes, ensuring higher visibility traffic enforcement. This new method allows the police to more effectively allocate their limited resources. Although the DDACTS model has significantly reduced crime and crash rates in the United States, it is necessary to thoroughly study its effects before applying it in other parts of the world; the factors that influence crime, crashes, and police patrol systems in the United States may differ significantly from those in, for instance, Asia. In the present research, Taiwan was chosen as an initial area of study because of the nation’s open data policy and good quality of the data available. This study focused on two key differences between the United States and Taiwan: (1) the cluster distributions of crash and crime events, and (2) possible effectiveness of DDACTS in these two regions. ArcGIS was used to calculate point cluster patterns and identify hotspots. Although the point patterns for crimes and crashes varied greatly between Texas and Taiwan, all pairs of crash and crime hotspots were in close proximity to one another. Thus, DDACTS may be effective for improving patrol efficiency in Taiwan, despite the nation’s significant socioeconomic differences with the United States Consequently, the results show that DDACTS may be efficient in various regions with different socioeconomic structures than the United States, such as countries in Asia. In the future, researchers from other nations may be able to use these results to revise and adjust their own DDACTS patrol plans.

Online Planning for Multiagent Situational Information Gathering in the Markov Environment

It is a challenging problem to make the team of unmanned aerial vehicles perform well to gather up-to-date situational awareness in dynamic environments. To solve the challenge, in this paper, a novel algorithm is presented on the multiagent information gathering problem. First, the physical environment is modeled as an undirected graph, where information obeying the multistate Markov chain is distributed in each vertex. Each agent is allocated in a designated area, where the objective of the team is to gather as much valuable information as possible. Second, this problem is formulated as the factored multiagent partially observable Markov decision process. Furthermore, a scalable centralized online planning algorithm is proposed by iteratively computing patrolling routes for each agent in a greedy fashion. These patrols are bounded optimal in specific conditions. In addition, we evaluate the algorithm on multiagent information gathering scenarios empirically by benchmarking it against some state-of-the-art online planning solvers, i.e., partially observable Monte Carlo planning and factor-value partially observable Monte Carlo planning. Experimental results show that the algorithm typically performs at least 9.04% better than these solvers in the four-agent patrolling problem with coupling degree being three, and can scale up to 100 agents with complex coupling relationships.

An Interactive Visual System for Generating Striking Pseudo Base Stations Decisions

The Pseudo base stations cause a great number of social and economic security problems with spreading spam messages. However, it is a serious challenge for security department to recognize the behavior patterns of pseudo base stations, then make use of limited police resources to prevent their activities and even to arrest related criminals. In this paper, a novel visual analytic approach to reveal the spatial-temporal behavior patterns of pseudo base stations is presented. In this approach, it takes advantage of density clustering over high frequency positions, and curve fitting on each cluster. This system supports decision makers to formulate patrol routes on the basis of above approximate trajectory. This approach has been demonstrated effective by means of case studies of authority data and has been evaluated novel and valuable in the view of domain experts.

Solving partially observable agent‐intruder games with an application to border security problems

AbstractIn this paper, we introduce partially observable agent‐intruder games (POAIGs). These games model dynamic search games on graphs between security forces (an agent) and an intruder given possible (border) entry points and high value assets that require protection. The agent faces situations with dynamically changing, partially observable information about the state of the intruder and vice versa. The agent may place sensors at selected locations, while the intruder may recruit partners to observe the agent's movement. We formulate the problem as a two‐person zero‐sum game, and develop efficient algorithms to compute each player's optimal strategy. The solution to the game will help the agent choose sensor locations and design patrol routes that can handle imperfect information. First, we prove the existence of ϵ‐optimal strategies for POAIGs with an infinite time horizon. Second, we introduce a Bayesian approximation algorithm to identify these ϵ‐optimal strategies using belief functions that incorporate the imperfect information that becomes available during the game. For the solutions of large POAIGs with a finite time horizon, we use a solution method common to extensive form games, namely, the sequence form representation. To illustrate the POAIGs, we present several examples and numerical results.

Strategically Patrolling in a Chemical Cluster Addressing Gas Pollutants' Releases through a Game-Theoretic Model.

Chemical production activities in chemical clusters, if not well managed, will pose great threats to the surrounding air environment and impose great burden on emergency handling. Therefore, it is urgent and substantial in a chemical cluster to develop proper and suitable pollution controlling strategies for an inspection agency to monitor chemical production processes. Apart from the static monitoring resources (e.g., monitoring stations and gas sensor modules), patrolling by mobile vehicle resources is arranged for better detecting the illegal releasing behaviors of emission spots in different chemical plants. However, it has been proven that the commonly used patrolling strategies (i.e., the fixed route strategy and the purely randomized route strategy) are non-optimal and fail to interact with intelligent chemical plants. Therefore, we proposed the Chemical Cluster Environmental Protection Patrolling (CCEPP) game to tackle the problem in this paper. Through combining the source estimation process, the game is modeled to detect the illegal releasing behaviors of chemical plants by randomly and strategically arranging the patrolling routes and intensities in different chemical sites. In this game-theoretic model, players (patroller and chemical sites), strategies, payoffs, and game solvers are modeled in sequence. More importantly, this game model also considers traffic delays or bounded cognition of patrollers on patrolling plans. Therefore, a discrete Markov decision process was used to model this stochastic process. Further, the model is illustrated by a case study. Results imply that the patrolling strategy suggested by the CCEPP game outperforms both the fixed route strategy and the purely randomized route strategy.

Optimization of Base Location and Patrol Routes for Unmanned Aerial Vehicles in Border Intelligence, Surveillance, and Reconnaissance

The location-routing problem (LRP) of unmanned aerial vehicles (UAV) in border patrol for Intelligence, Surveillance, and Reconnaissance is investigated, where the locations of UAV base stations and the UAV flying routes for visiting the targets in border area are jointly optimized. The capacity of the base station and the endurance of the UAV are considered. A binary integer programming model is developed to formulate the problem, and two heuristic algorithms combined with local search strategies are designed for solving the problem. The experiment design for simulating the distribution of stations and targets in border is proposed for generating random test instances. Also, an example based on the practical border in Guangxi is presented to illustrate the problem and the solution approach. The performance of the two algorithms is analysed and compared through randomly generated instances.

Genetic Algorithm for Optimizing Routing Design and Fleet Allocation of Freeway Service Overlapping Patrol

The freeway service patrol problem involves patrol routing design and fleet allocation on freeways that would help transportation agency decision-makers when developing a freeway service patrols program and/or altering existing route coverage and fleet allocation. Based on the actual patrol process, our model presents an overlapping patrol model and addresses patrol routing design and fleet allocation in a single integrated model. The objective is to minimize the overall average incident response time. Two strategies—overlapping patrol and non-overlapping patrol—are compared in our paper. Matrix encoding is applied in the genetic algorithm (GA), and to maintain population diversity and avoid premature convergence, a niche strategy is incorporated into the traditional genetic algorithm. Meanwhile, an elitist strategy is employed to speed up the convergence. Using numerical experiments conducted based on data from the Sioux Falls network, we clearly show that: overlapping patrol strategy is superior to non-overlapping patrol strategy; the GA outperforms the simulated annealing (SA) algorithm; and the computational efficiency can be improved when LINGO software is used to solve the problem of fleet allocation.

Patrol Route Planning for Incident Response Vehicles under Dispatching Station Scenarios

AbstractTraffic incidents often contribute to major safety concerns, impose additional congestion in the neighboring transportation networks, and induce indirect costs to economy. As roughly a third of traffic crashes are secondary accidents, effective incident management activities are critical, especially on roadways with high traffic volume, to detect, respond to, and clean up incidents in a timely fashion, which supports safety constraints and restores traffic capacity in the transportation network. Hence, it is beneficial to simultaneously plan for first respondents’ dispatching station location and patrol route design to mitigate congestion. This article presents an optimal route planning for patrolling vehicles to facilitate quick response to potential accidents. A mixed‐integer nonlinear program is proposed that minimizes the respondents’ patrolling travel cost based on the expected maximum response time from each arbitrary location to all incident locations (a.k.a. hotspots) with various incident occurrence probabilities. We have developed a column generation‐based solution technique to solve the route optimization model under different station design scenarios. To investigate the impact of dispatching station design on the routing cost, an integrated genetic algorithm framework with embedded continuous approximation approach is developed that reduces the complexity of the hybrid location design and route planning problem. Numerical experiments on hypothetical networks of various sizes are conducted to indicate the performance of the proposed algorithm and to draw managerial insights. The models and solution techniques, developed in this article, are applicable to a number of network problems that simultaneously involve routing and facility location choices.

CCP game: A game theoretical model for improving the scheduling of chemical cluster patrolling

Chemical clusters can be attractive targets for terrorism, due to the extreme importance of them as well as due to the existence of dangerous materials. Patrolling is scheduled for better securing chemical clusters. However, the current patrolling strategies fail on competing with intelligent attackers and therefore can be non-optimal. The so-called chemical cluster patrolling (CCP) game is proposed in this paper. The CCP game employs game theory as a methodology, aiming at randomly but strategically scheduling security patrols in chemical clusters. The patroller and the attacker are modelled as the two rational players in the CCP game. The patroller's strategy is defined as probabilistically traveling within the cluster or patrolling some plants while the attacker's strategy is formulated as a combination of an attack target, the start time of the attack, and the attack scenario to be used. The Stackelberg equilibrium and a robust solution which takes into consideration of the patroller's distribution-free uncertainties on the attacker's parameters are defined for predicting the outcome of the CCP game. Results of the case study indicate that the patrolling strategy suggested by the CCP game outperforms both the fixed patrolling route strategy and the purely randomized patrolling strategy.