Patrol Routes Research Articles

Spatio-Temporal Model for Wildlife Poaching Prediction Evaluated Through a Controlled Field Test in Uganda

Worldwide, conservation agencies employ rangers to protect conservation areas from poachers. However, agencies lack the manpower to have rangers effectively patrol these vast areas frequently. While past work has modeled poachers’ behavior so as to aid rangers in planning future patrols, those models’ predictions were not validated by extensive field tests. In my thesis, I present a spatio-temporal model that predicts poaching threat levels and results from a five-month field test in Uganda’s Queen Elizabeth Protected Area (QEPA). To my knowledge, this is the first time that a predictive model has been evaluated through such an extensive field test in this domain. These field test will be extended to another park in Uganda, Murchison Fall Protected Area, shortly. Main goals of my thesis are to develop the best performing model in terms of speed and accuracy and use such model to generate efficient and feasible patrol routes for the park rangers.

Traveling Officer Problem: Managing Car Parking Violations Efficiently Using Sensor Data

The on-street parking system is an indispensable part of civic projects, as it provides travelers and shoppers with parking spaces. With the recent in-ground sensors deployed throughout the Melbourne central business district (CBD), there is a significant problem on how to use the sensor data to manage parking violations and issue infringement notices efficiently in a short time-window. In this paper, we use a large real-world dataset with on-street parking sensor data from the local city council, and establish a formulation of the traveling officer problem with a general probability-based model. We propose two solutions using a spatio-temporal probability model for parking officers to maximize the number of infringing cars caught with limited time cost. Using real-world parking sensor data and Google Maps road network information, the experimental results show that our proposed algorithms outperform the existing patrolling routes.

Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.

Design and fabrication of unmanned ground surveillance vehicle

Unmanned ground surveillance vehicle is a remote controlled vehicle used to survey the required environment at any terrain condition. The main objective of this vehicle is the Self Lifting mechanism that lifts itself when the vehicle has encountered a “topple” which is triggered by the user far away from the vehicle. This UGSV not only can save manpower but also ensure the operation of surveillance being well performed. Due to the limitation of manpower and the fixed camera positions, using surveillance is different from the traditional patrolling system. The Unmanned Ground Surveillance Vehicle which can be able to maneuver automatically to a wider range and record the monitored image within a predefined patrolling route to improve the performance of the traditional patrolling system. Besides, UGSV can be connected to the mobile device, Laptop at anytime and anywhere. Furthermore, the vehicle can be remote controlled by user to move to the position to get the indoor image we want. The AV-CAM is also mounted on the proposed robot to record the images and transmit them back by radio waves.

Tungiasis: Outbreak investigation of a zoonosis during overseas deployment

BackgroundTungiasis is an ectoparasitosis caused by the sand flea Tunga penetrans. It is endemic in the under privileged communities of Latin America, the Caribbean and Sub Saharan Africa with geographic and seasonal variations even within endemic areas. We describe investigation of an outbreak of Tungiasis in troops deployed as part of UN peacekeeping force in Central Africa. MethodsTungiasis was diagnosed in an unusually large number of cases of severely pruritic boils over feet in soldiers of a UN peacekeeping battalion. An outbreak investigation was carried out and the outbreak was described in time, place and person distribution. A retrospective cohort study was done to ascertain the associated risk factors. ResultsA total of 36 cases were identified of which 33 had laboratory confirmation. Of the 36 cases, 10(27.77%) had only Fortaleza Stage II lesions, 22 (61.11%) a combination of Fortaleza Stage II and III lesions and four (11.11%) cases had a combination of Stage, II, III and IV lesions. Secondary bacterial infection was seen in 25 (69.44%) cases. Epidemiological analysis revealed that it was a common source single exposure outbreak traced to a temporary campsite along one of the patrolling routes. ConclusionIn a Military setting an integrated approach combining health education and environmental control is required to prevent such outbreaks.

Predictive police patrolling to target hotspots and cover response demand

Police forces are constantly competing to provide adequate service whilst faced with major funding cuts. The funding cuts result in limited resources hence methods of improving resource efficiency are vital to public safety. One area where improving the efficiency could drastically improve service is the planning of patrol routes for incident response officers. Current methods of patrolling lack direction and do not consider response demand. Police patrols have the potential to deter crime when directed to the right areas. Patrols also have the ability to position officers with access to high demand areas by pre-empting where response demand will arise. The algorithm developed in this work directs patrol routes in real-time by targeting high crime areas whilst maximising demand coverage. Methods used include kernel density estimation for hotspot identification and maximum coverage location problems for positioning. These methods result in more effective daily patrolling which reduces response times and accurately targets problem areas. Though applied in this instance to daily patrol operations, the methodology could help to reduce the need for disaster relief operations whilst also positioning proactively to allow quick response when disaster relief operations are required.

Optimal patrol routes: interdicting and pursuing rhino poachers

Interdiction patrolling (also known as hot spots policing) is a proactive police operation that depends on good planning. And the pursuit of fleeing suspects is a challenging operation that all police forces engage in on a regular basis. We give software tools that improve the effectiveness of both. We demonstrate the effectiveness of our tools by applying them to the case of rhino poaching in wildlife reserves. An interdiction patrol pre-emptively seeks out poaching parties within a reserve. Upon picking up the trail of a poaching party, a pursuit team needs a pursuit strategy that optimizes their chances of an interception. Our interdiction patrol route tool identifies optimal interdiction patrol routes to walk. This tool is based on a Stackelberg game and represents different types of poachers and their utilities. These utilities are updated (learned) each time new information on poaching party routes is added to the database. Our second tool provides a real-time sequence of team member moves that maximizes the team’s chances of apprehending a pursued party. We show that our tools perform well when applied to simulated and real data.

Developing an online cooperative police patrol routing strategy

A cooperative routing strategy for daily operations is necessary to maintain the effects of hotspot policing and to reduce crime and disorder. Existing robot patrol routing strategies are not suitable, as they omit the peculiarities and challenges of daily police patrol including minimising the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability to patrol routes. In this research, we propose a set of guidelines for patrol routing strategies to meet the challenges of police patrol. Following these guidelines, we develop an innovative heuristic-based and Bayesian-inspired real-time strategy for cooperative routing police patrols. Using two real-world cases and a benchmark patrol strategy, an online agent-based simulation has been implemented to testify the efficiency, flexibility, scalability, unpredictability, and robustness of the proposed strategy and the usability of the proposed guidelines.

Deploying PAWS to Combat Poaching: Game-Theoretic Patrolling in Areas with Complex Terrain (Demonstration)

The conservation of key wildlife species such as tigers and elephants are threatened by poaching activities. In many conservation areas, foot patrols are conducted to prevent poaching but they may not be well-planned to make the best use of the limited patrolling resources. While prior work has introduced PAWS (Protection Assistant for Wildlife Security) as a game-theoretic decision aid to design effective foot patrol strategies to protect wildlife, the patrol routes generated by PAWS may be difficult to follow in areas with complex terrain. Subsequent research has worked on the significant evolution of PAWS, from an emerging application to a regularly deployed software. A key advance of the deployed version of PAWS is that it incorporates the complex terrain information and generates a strategy consisting of easy-to-follow routes. In this demonstration, we provide 1) a video introducing the PAWS system; 2) an interactive visualization of the patrol routes generated by PAWS in an example area with complex terrain; and 3) a machine-human competition in designing patrol strategy given complex terrain and animal distribution.

Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security

Poaching is a serious threat to the conservation of key species and whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of limited patrolling resources. To remedy this situation, prior work introduced a novel emerging application called PAWS (Protection Assistant for Wildlife Security); PAWS was proposed as a game-theoretic (“security games”) decision aid to optimize the use of patrolling resources.This paper reports on PAWS’s significant evolution from a proposed decision aid to a regularly deployed application, reporting on the lessons from the first tests in Africa in Spring 2014, through its continued evolution since then, to current regular use in Southeast Asia and plans for future worldwide deployment. In this process, we have worked closely with two NGOs (Panthera and Rimba) and incorporated extensive feedback from professional patrolling teams. We outline key technical advances that lead to PAWS’s regular deployment: (i) incorporating complex topographic features, e.g., ridgelines, in generating patrol routes; (ii) handling uncertainties in species distribution (game theoretic payoffs); (iii) ensuring scalability for patrolling large-scale conservation areas with fine-grained guidance; and (iv) handling complex patrol scheduling constraints.

Optimal Patrol Planning Using STEN for Road Maintenance

The authors propose a method for road maintenance patrol planning using multiple vehicles, which is modeled as an integer-linear problem. Specifically, the authors consider a model for reducing the number of patrol vehicles and optimizing the patrol routes. Difficulties arise in solving such routing problems because of sub-tour elimination with multiple vehicles. To address this sub-tour problem, the authors use the Space-Time Extended Network (STEN). The authors investigate a virtual network using the proposed method, and describe the behavior of the proposed model. The time required for the calculations is short, so that the system is suitable for practical applications.

MULTI-VEHICLE COVERING TOUR PROBLEM: BUILDING ROUTES FOR URBAN PATROLLING

In this paper we study a particular aspect of the urban community policing: routine patrol route planning. We seek routes that guarantee visibility, as this has a sizable impact on the community perceived safety, allowing quick emergency responses and providing surveillance of selected sites (e.g., hospitals, schools). The planning is restricted to the availability of vehicles and strives to achieve balanced routes. We study an adaptation of the model for the multi-vehicle covering tour problem, in which a set of locations must be visited, whereas another subset must be close enough to the planned routes. It constitutes an NP-complete integer programming problem. Suboptimal solutions are obtained with several heuristics, some adapted from the literature and others developed by us. We solve some adapted instances from TSPLIB and an instance with real data, the former being compared with results from literature, and latter being compared with empirical data.

아동안전지킴이 제도의 실효성 향상 방안 -직무만족에 미치는 영향을 중심으로-

아동안전지킴이 제도는 아동의 범죄피해 방지를 위해 경찰청 주관으로 전국에서 운영되고 있는 협력치안 프로그램이다. 아동안전지킴이 제도는 2009년 시행된 이래 아동범죄 예방효과에 긍정적 영향을 미쳤을 것으로 기대되고 있으나 구체적 효과성은 불분명하다. 운영현황에 대한 실증적 연구와 제도발전을 위한 학술적 검토가 충분하지 못했기 때문이다. 이러한 배경에서 본 연구는 아동안전지킴이 제도의 운영실태를 파악하고 실효성 향상방안을 탐색하기 위한 목적에서 수행되었다. 연구목적 달성을 위해 16개 지역 지방경찰청에 배치된 전체 아동안전지킴이에 대해 할당표본을 추출하여 설문조사를 실시했다. 설문결과는 빈도분석, 티검증, 아노바, 다중회귀분석을 실시했다. 분석결과 제도발전을 위해 아동안전지킴이집과의 연계강화, 아동안전지킴이의 순찰노선에 대한 참여적 편성, 교육관리 감독체계의 개선, 활동비의 적정성, 성공적 직무인식 증진을 위한 아동접촉근무 강화방안 등의 중요성이 검증되었다. According to the children's crime risk is increased, national police agency has implemented child safety keeper system since 2009. Despite the importance of child safety keeper studies, there was no seeking of empirical research and development plan for the operation. The purpose of the study is to find ways to improve the effectiveness of the child safety keeper system. To achieve the research objectives, all the child safety keepers were surveyed by quota sampling in the 16 provincial police agency. Questionnaire analyzed by frequency analysis, t-test, Avova, multiple regression analysis. The result of analysis is the need to strengthen links with child safety keeping house, participatory setting for the patrol route, the improvement of education management and supervision, adequacy of allowance, strengthening contact with children for job success awareness.

DESIGNING DAILY PATROL ROUTES FOR POLICING BASED ON ANT COLONY ALGORITHM

Abstract. In this paper, we address the problem of planning police patrol routes to regularly cover street segments of high crime density (hotspots) with limited police forces. A good patrolling strategy is required to minimise the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability in patrol routes. Previous studies have designed different police patrol strategies for routing police patrol, but these strategies have difficulty in generalising to real patrolling and meeting various requirements. In this research we develop a new police patrolling strategy based on Bayesian method and ant colony algorithm. In this strategy, virtual marker (pheromone) is laid to mark the visiting history of each crime hotspot, and patrollers continuously decide which hotspot to patrol next based on pheromone level and other variables. Simulation results using real data testifies the effective, scalable, unpredictable and extensible nature of this strategy.

A minimum cost network flow model for the maximum covering and patrol routing problem

This paper shows how the maximum covering and patrol routing problem (MCPRP) can be modeled as a minimum cost network flow problem (MCNFP). Based on the MCNFP model, all available benchmark instances of the MCPRP can be solved to optimality in less than 0.4s per instance. It is furthermore shown that several practical additions to the MCPRP, such as different start and end locations of patrol cars and overlapping shift durations can be modeled by a multi-commodity minimum cost network flow model and solved to optimality in acceptable computational times given the sizes of practical instances.

Persistent Monitoring of Events With Stochastic Arrivals at Multiple Stations

This paper introduces a new mobile sensor scheduling problem involving a single robot tasked to monitor several events of interest that are occurring at different locations (stations). Of particular interest is the monitoring of transient events of a stochastic nature, with applications ranging from natural phenomena (e.g., monitoring abnormal seismic activity around a volcano using a ground robot) to urban activities (e.g., monitoring early formations of traffic congestion using an aerial robot). Motivated by examples like these, this paper focuses on problems in which the precise occurrence times of the events are unknown apriori, but statistics for their interarrival times are available. In monitoring such events, the robot seeks to: (1) maximize the number of events observed and (2) minimize the delay between two consecutive observations of events occurring at the same location. This paper considers the case when a robot is tasked with optimizing the event observations in a balanced manner, following a cyclic patrolling route. To tackle this problem, first, assuming that the cyclic ordering of stations is known, we prove the existence and uniqueness of the optimal solution and show that the solution has desirable convergence rate and robustness. Our constructive proof also yields an efficient algorithm for computing the unique optimal solution with O(n) time complexity, in which n is the number of stations, with O(log n) time complexity for incrementally adding or removing stations. Except for the algorithm, our analysis remains valid when the cyclic order is unknown. We then provide a polynomial-time approximation scheme that computes for any ε > 0 a (1 + ε)-optimal solution for this more general, NP-hard problem.

The influence of experience upon information-sampling and decision-making behaviour during risk assessment in military personnel

We examined the influence of experience upon information-sampling and decision-making behaviour in a group of military personnel as they conducted risk assessments of scenes photographed from patrol routes during the recent conflict in Afghanistan. Their risk assessment was based on an evaluation of Potential Risk Indicators (PRIs) during examination of each scene. We found that both participant groups were equally likely to fixate PRIs, demonstrating similarity in the selectivity of their information-sampling. However, the inexperienced participants made more revisits to PRIs, had longer response times, and were more likely to decide that the scenes contained a high level of risk. Together, these results suggest that experience primarily modulates decision-making behaviour. We discuss potential routes to train personnel to conduct risk assessments in a more similar manner to experienced participants.

A risk probability-map generation model on multimedia services environment

The rapid growth of modern society has been a double-edged sword; it has led to positive results such as income growth due to the diversification of our society, but also negative results such as an increase in crime. For this reason, cities are confronting a wide range of issues. Social issues, especially criminal offenses, stir up more fear of crime among residents. The rapid societal penetration of digital devices such as smart phones, along with an increase in IT knowledge, has made our lives more convenient. However, cyber-crime and violent crime taking advantage of these benefits are also increasing. There is an increasing need for crime prevention and crime prediction in order to solve these problems, and many studies on crime are under way as part of our effort to respond to various changes in our society using a variety of prediction tools. Thus, in this study, a combined risk probability map generation model was suggested by predicting crime frequency through a Markov Chain Analysis, quantifying risks through objective classification of urban spaces and applying attribute-specific risk indexes and interpretation keys across the entire scope of the study. The crime prediction model based on risk probability map suggested in this study facilitates multimedia services using mobile devices such as smartphones and thus can be used to optimally plan patrol routes of police officers in zones vulnerable to crimes as well as placement of surveillance systems, which will in turn contribute to relieving many citizens' anxiety about crime. Our model's approach is too small environment in this research time. But we will try to more experiment environment is big such as town and state next research time.

An improved formulation for the maximum coverage patrol routing problem

We present an improved formulation for the maximum coverage patrol routing problem (MCPRP). The main goal of the patrol routing problem is to maximize the coverage of critical highway stretches while ensuring the feasibility of routes and considering the availability of resources. By investigating the structural properties of the optimal solution, we formulate a new, improved mixed integer program that can solve real life instances to optimality within seconds, where methods proposed in prior literature fail to find a provably optimal solution within an hour. The improved formulation provides enhanced highway coverage for both randomly generated and real life instances. We show an average increase in coverage of nearly 20% for the randomly generated instances provided in the literature, with a best case increase over 46%. Similarly, for the real life instances, we close the optimality gap within seconds and demonstrate an additional coverage of over 13% in the best case. The improved formulation also allows for testing a number of real life scenarios related to multi-start routes, delayed starts at the beginning of the shifts, and taking a planned break during the shift. Being able to solve these scenarios in short durations help decision and policy makers to better evaluate resource allocation options while serving public.

Bi-criteria dynamic location-routing problem for patrol coverage

In this paper, we address the problem of dynamic patrol routing for state troopers for effective coverage of highways. Specifically, a number of state troopers start their routes at temporary stations (TS), patrol critical locations with high crash frequencies, and end their shifts at other (or the same) TS so the starting points for the next period are also optimized. We determine the number of state troopers, their assigned routes, and the locations of the TS where they start and end their routes. The TS are selected from a given set of potential locations. The problem, therefore, is a multi-period dynamic location-routing problem in the context of public service. Our objective is to maximize the critical location coverage benefit while minimizing the costs of TS selections, vehicle utilizations, and routing/travel. The multi-objective nature of the problem is handled using an ɛ-constraint approach. We formulate the problem as a mixed integer linear programming model and solve it using both off-the-shelf optimization software and a custom-built, efficient heuristic algorithm. The heuristic, utilizing the hierarchical structure of the problem, is built on the decomposition of location and routing problems. By allowing routing to start from multiple locations, our model improves the coverage by as much as 12% compared with the single-depot coverage model.