Patrol Routes Research Articles

Enhancing Urban Safety: Optimal Patrol Route Strategies for Volunteer Security Squads Based on Integrated BIM-GIS Data

Enhancing Urban Safety: Optimal Patrol Route Strategies for Volunteer Security Squads Based on Integrated BIM-GIS Data

Analysis of wildlife threat findings based on the SMART patrol application at Pemerihan Resort, Bukit Barisan Selatan National Park

Monitoring, managing, and maintaining conservation areas are challenges faced by various parties. The Spatial Monitoring and Reporting Tool (SMART) is an application used to measure, collect, evaluate, and improve the effectiveness of location-based monitoring and conservation activities and has been implemented at the Pemerihan Resort, Bukit Barisan Selatan National Park (TNBBS). Based on the SMART application, the research aims to analyze the findings of threats and wildlife on active patrol routes. The methods used are documentary studies and field observations. The data obtained from the documentary study is in the form of data from forest patrols for two years (2020-2022), then processed, classified, and analyzed into data on threat findings and wild animal encounter data directly or through signs of presence, which is then verified through field observations. The research results show that the data found from the SMART application during four patrol periods from 2020-2022 consisted of 143 threat findings and 841 wild animal encounters. The highest threat finding was animal hunting, with 56 findings (39%), and the lowest was logging, with three findings (2%). Twenty-three wild animal encounters were found directly or through signs of presence such as tracks, feces, scratches, and food remain. The highest wildlife encounter was the sambar deer (Rusa unicolor). The high threat findings indicate much human activity in the Pemerihan Resort Area, TNBBS, which can disturb wildlife and its habitats.

Probabilistic Path Planning for UAVs in Forest Fire Monitoring: Enhancing Patrol Efficiency through Risk Assessment

Forest fire is a significant global natural disaster, and unmanned aerial vehicles (UAVs) have gained attention in wildfire prevention for their efficient and flexible monitoring capabilities. Proper UAV patrol path planning can enhance fire-monitoring accuracy and response speed. This paper proposes a probabilistic path planning (PPP) module that plans UAV patrol paths by combining real-time fire occurrence probabilities at different points. Initially, a forest fire risk logistic regression model is established to compute the fire probabilities at different patrol points. Subsequently, a patrol point filter is applied to remove points with low fire probabilities. Finally, combining fire probabilities with distances between patrol points, a dynamic programming (DP) algorithm is employed to generate an optimal UAV patrol route. Compared with conventional approaches, the experimental results demonstrate that the PPP module effectively improves the timeliness of fire monitoring and containment, and the introduction of DP, considering that the fire probabilities and the patrol point filter both contribute positively to the experimental outcomes. Different combinations of patrol point coordinates and their fire probabilities are further studied to summarize the applicability of this method, contributing to UAV applications in forest fire monitoring and prevention.

A Deep Reinforcement Learning Algorithm for Trajectory Planning of Swarm UAV Fulfilling Wildfire Reconnaissance

Wildfires have long been one of the critical environmental disasters that require a careful monitoring system. An intelligent system has the potential to both prevent/extinguish the fire and deliver urgent requirements postfire. In recent years, unmanned aerial vehicles (UAVs), with the ability to detect missions in high-risk areas, have been gaining increasing interest, particularly in forest fire monitoring. Taking a large-scale area involved in a fire into consideration, a single UAV is often insufficient to accomplish the task of covering the whole disaster zone. This poses the challenge of multi-UAVs optimum path planning with a key focus on limitations such as energy constraints and connectivity. To narrow down this issue, this paper proposes a deep reinforcement learning-based trajectory planning approach for multi-UAVs that permits UAVs to extract the required information within the disaster area on time. A target area is partitioned into several identical subareas in terms of size to enable UAVs to perform their patrol duties over the subareas. This subarea-based arrangement converts the issue of trajectory planning into allowing UAVs to frequently visit each subarea. Each subarea is initiated with a risk level by creating a fire risk map optimizing the UAV patrol route more precisely. Through a set of simulations conducted with a real trace of the dataset, the performance outcomes confirmed the superiority of the proposed idea.

Improving Effectiveness of the Safety Service Patrol Programs: A Discrete Event-Based Simulation Approach

Safety service patrols (SSPs) play an important role in incident management on highways. It is critical to respond to incidents in a timely manner as this can significantly reduce nonrecurrent congestion and improve safety. Therefore, it is essential to allocate available SSP vehicles to highway segments such that their effectiveness is maximized. This study aimed to develop a simulation-based framework to assist with SSP service optimization. More specifically, a discrete event-based simulation tool (i.e., SSP-OPT) with customizable parameters was developed to help plan the optimum patrol routes based on available SSP resources and predicted incidents. The developed tool was tested with roadway traffic and incident data from the Virginia highway network. After model calibration, the simulation results showed that the developed SSP-OPT tool could replicate the patrol routes with similar performance to the field observations, validating the tool. Further, adopting the tool for corridor-level optimization could help to identify the best patrol plan to minimize SSP response time and maximize SSP response rates for a given number of SSP vehicles. The SSP-OPT tool requires minimal user input (e.g., segment lengths, annual average daily traffic) and has the flexibility to be easily applied to any highway corridor once calibrated. The tool generates various performance metrics to enable more informed decision making in SSP route planning.

A Study on Combating Illegal Trade in Green Sea Turtles based on a Multi-Model Strategy

This study addresses the long-standing and increasingly complex issue of global illegal wildlife trade, proposing a system solution aimed at monitoring and combating this trade, with a special emphasis on the protection of green sea turtles. The ARIMA Predict Model was utilized to demonstrate that increased enforcement efforts and higher operational expenses are significantly associated with reduced volumes of illegal wildlife trade. An illegal trade risk model was developed, employing SLSQP to enhance accuracy, constrained by budget allocation, maximum RFID reader capacity, and device failure rates. Furthermore, the Sea Turtle Movement Model was crafted by examining seasonal behaviors, agent-based movement patterns, temperature preferences, stochastic elements, and simulation time steps, enabling the delineation of migratory paths of sea turtles across different seasons. Ant Colony Optimization (ACO) was applied to optimize patrol routes. The findings underscored the necessity for additional governmental support, encompassing legal authorization, environmental access, international collaboration, and financial backing. The project’s effectiveness was assessed using the Risk Reduction Coefficient Model and Monte Carlo simulations, which significantly reduced illegal trade by 34.43%, with projections indicating continued declines over the subsequent five years.

Smart city planning focused on the US cities in need of policing innovations and public health safety technologies and strategies

The issue of firearm-related injuries, deaths and crime in the United States is of particular relevance. Every year, about 32,000 lives are lost as a result of firearm-related incidents, including self-inflicted injuries. Other 74,000 people suffer non-fatal gunshot wounds. Addressing this multi-faceted problem is possible through a comprehensive and integrated approach that encompasses public health initiatives, law enforcement efforts, and security measures. In the context of solving this problem, the concepts of “community-oriented policing”, “problem-oriented policing”, “broken windows theory”, “smart city social sustainability initiative” (multi-system improvement and meeting the needs of marginalised residents), “feedback methodology”, etc. are thoroughly considered. The author proposes the strategic deployment of mini-police stations in areas with a high crime rate as proper centres of police visibility and a deterrent to criminals. To realise this task, it is necessary to integrate the latest approaches to Smart Urban Development with innovative urban planning technologies that would increase the influence of law enforcement agencies in the process of protecting public health and safety in cities. In particular, the article proposes the following for implementation: identifying crime hotspots by means of advanced geographic information systems, applying machine learning algorithms to predict future crime trends, using advanced real-time surveillance technologies and firearms detection systems, applying geospatial analysis to optimise patrol routes, dynamically adjusting patrol routes depending on real-time data analytics, deploying mobile command centres. As a result, law enforcement agencies can effectively communicate to potential criminals that their actions will not go unnoticed. They may achieve significant success in improving the overall quality of life of residents in such areas. Also, they will create safer communities, and reduce the number of incidents involving the use of firearms.

Traveling of multiple salesmen to dynamically changing locations for satisfying multiple goals

Polymer grade scheduling, maritime surveillance, e-food delivery, e-commerce, and military tactics necessitate multiple agents (e.g., extruders, speed boats, salesmen) capable of visiting (or completing) dynamically changing locations (or tasks) in minimum time and distance. This study proposes a novel methodology based on clustering and local heuristic-based evolutionary algorithms to address the dynamic traveling salesman problem (TSP) and the dynamic multi-salesman problem with multiple objectives. The proposed algorithm is evaluated on 11 benchmark TSP problems and large-scale problems with up to 10,000 instances. The results show the superior performance of the proposed methodology called the dynamic two-stage evolutionary algorithm as compared to the dynamic hybrid local search evolutionary algorithm. Furthermore, the algorithm's applicability is illustrated through various scenarios involving up to four salesmen and three objectives with dynamically changing locations. To demonstrate real-world relevance, a maritime surveillance problem employing a helideck monitoring system is solved, wherein the objective is to minimize the patrolling route while visiting faulty vessels that threaten marine vessels. This study provides a general framework of TSP which finds application in several sectors, including planning and scheduling in chemical and manufacturing industries, the defense sector, and the e-commerce sector. Finally, the results showcase the effectiveness of the proposed methodology in solving the dynamic multiobjective, and multiple salesmen problem, which represents a more generalized version of the TSP.

Who Let the Guards Out: Visual Support for Patrolling Games.

Effective security patrol management is critical for ensuring safety in diverse environments such as art galleries, airports, and factories. The behavior of patrols in these situations can be modeled by patrolling games. They simulate the behavior of the patrol and adversary in the building, which is modeled as a graph of interconnected nodes representing rooms. The designers of algorithms solving the game face the problem of analyzing complex graph layouts with temporal dependencies. Therefore, appropriate visual support is crucial for them to work effectively. In this paper, we present a novel tool that helps the designers of patrolling games explore the outcomes of the proposed algorithms and approaches, evaluate their success rate, and propose modifications that can improve their solutions. Our tool offers an intuitive and interactive interface, featuring a detailed exploration of patrol routes and probabilities of taking them, simulation of patrols, and other requested features. In close collaboration with experts in designing patrolling games, we conducted three case studies demonstrating the usage and usefulness of our tool. The prototype of the tool, along with exemplary datasets, is available at https://gitlab.fi.muni.cz/formela/strategy-vizualizer.

Research and Application of UAV Nest Dispatching Management System of the Transmission Line

To address the issues of insufficient research and unsatisfactory replacement effects of unmanned aerial vehicle (UAV) hangar for transmission line inspection, a dispatch management system and method for UAV hangar for transmission line inspection are proposed. Combined with multi-UAV hangar control, patrol route planning, unmanned electromechanical energy supplement, remote accurate take-off and landing and other technologies, remote control and centralized dispatching management of UAV hangar are realized, and a new mode of transmission line intelligent patrol operation of UAV hangar is established. In the application practice of transmission line inspection in Foshan area, this system has achieved intelligent inspection of UAV hangar, greatly improving the quality and efficiency of power grid inspection, and providing strong technical support for enhancing the comprehensive ability of power inspection automation and achieving unmanned transmission line inspection.

A Hybrid Discrete Grey Wolf Optimizer with Local Search for Multi-UAV Patrolling

This paper addresses the multi- UAV patrolling problem, a NP-hard optimization problem that is focused on minimizing idleness, which is defined as the time between consecutive visits to specific locations. We propose the Discrete Grey Wolf Optimizer (D-GWO), which is specifically developed to handle the discrete aspects of UAV patrolling routes. This new algorithm is enhanced with a 2-opt local search strategy, which integrates the global search capabilities of D-GWO with the precision of local optimization to effectively refine solutions. Comparative experimental results show that our algorithm outperforms established methods such as ant colony optimization and simulated annealing in terms of reducing global worst idleness and overall exploration time. Our findings suggest that the D-GWO algorithm is particularly effective for complex multi-UAV patrolling tasks, significantly enhancing efficiency in security and disaster response missions.

PERANCANGAN APLIKASI MONITORING PATROLI SECURITY PADA PT. PLN UP2D PALEMBANG MENGGUNAKAN SOFTWARE DEVELOPMENT LIFE CYCLE (SDLC)

Patrol activities carried out by PT. PLN UP2D Palembang is currently still using manual reporting which often results in poor communication, intentional or not. Such as a lack of focus and deliberately not carrying out tasks as they should, which are expected to be optimal but beyond expectations. For this reason, the company needs to improve the way it monitors PT security officers. PLN UP2D Palembang so that patrol activities can be more efficient, systematic and structured. The aim of this research is to design an e-patrol security application that has added value, namely features that can make it easier for security officers to carry out reporting and communication activities between security officers more quickly. Furthermore, this application also provides guarantees for companies that security officers are really working according to their duties well and the reports sent by security officers are real time and can be checked immediately. The finalresult of this research is a design for an Android web-based patrol application that is run according to the rules and schedule, division of tasks, and available patrol routes.

Hotspots-based patrol route optimization algorithm for smart policing

Smart policing based on the analysis of big data ensures the development and sustainability of police policy. However, it is difficult to find instances in which the results of data analysis have been applied to actual policy in the field of crime prevention. The South Korean police force recognizes the need for smart policing and is engaged in various research and field support activities. Some examples that are especially relevant for crime investigation include analyzing the connections between cases and predicting the location of the next crime in a series of crimes and the location of suspects. However, it is difficult to find examples of police policy that use big data. Therefore, this study aims to suggest a model that uses big data to respond to emergency calls efficiently. First, we extract hotspots that are predicted to be locations of criminal activity based on an analysis of the association between community environment data and crime data. Second, we create a route having the shortest travel time to the crime location by developing a route optimization algorithm. Lastly, we assess the performance of the patrol routes in reflecting real-time traffic information. If the data application model suggested in this study could be adjusted and applied to the current police patrol system, the model could be used by each police department effectively.

Optimized design of patrol path for offshore wind farms based on genetic algorithm and particle swarm optimization with traveling salesman problem

SummaryWith the rapid expansion of global offshore wind power market, the research on improving the full life cycle income and reducing the construction and operation and maintenance costs has attracted the attention of scholars in the industry. In view of the different aging degree and maintenance cycle of wind turbines, this paper studies the optimized design of patrol path for offshore wind farms based on genetic algorithm (GA) and particle swarm optimization (PSO) with traveling salesman problem (TSP). Firstly, the problem of patrol routing planning in offshore wind farms is described as the traveling salesman problem of shortest route optimization. Secondly, the GA and PSO algorithms are simulated and verified separately, and the patrol path distance is taken as the objective function. Finally, through simulation experiments, the optimized patrol path performances of PSO and GA are compared, which can help to find a shortest route and reduce the operation and maintenance costs.

Smooth Autonomous Patrolling for a Differential-Drive Mobile Robot in Dynamic Environments.

Today, mobile robots have a wide range of real-world applications where they can replace or assist humans in many tasks, such as search and rescue, surveillance, patrolling, inspection, environmental monitoring, etc. These tasks usually require a robot to navigate through a dynamic environment with smooth, efficient, and safe motion. In this paper, we propose an online smooth-motion-planning method that generates a smooth, collision-free patrolling trajectory based on clothoid curves. Moreover, the proposed method combines global and local planning methods, which are suitable for changing large environments and enabling efficient path replanning with an arbitrary robot orientation. We propose a method for planning a smoothed path based on the golden ratio wherein a robot's orientation is aligned with a new path that avoids unknown obstacles. The simulation results show that the proposed algorithm reduces the patrolling execution time, path length, and deviation of the tracked trajectory from the patrolling route compared to the original patrolling method without smoothing. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity, and its performance was validated through the results of an experiment employing a differential-drive mobile robot.

The Hot Spot Coverage Patrol Problem: Formulations and Solution Approaches

When designing a patrol route, it is often necessary to pay more attention to locations with high crime rates. In this paper, we study a patrol routing problem for a fleet of patrol cars patrolling a region with a high-crime neighborhood (HCN) consisting of multiple hot spots. Considering the disorder and chaos in the HCN, at least one patrol car is required in the HCN at any given time during the patrol. We call this routing problem the hot spot coverage patrol problem (HSCPP). In the HSCPP, the importance of a patrol location is quantified by a prize, and the prize is collected if a patrol car visits the location. Our objective is to maximize the sum of prizes collected by the patrol cars, obeying all operational requirements. We propose mathematical formulations and develop several solution approaches for the HSCPP. The global approach consists of finding the routing solution for all patrol cars with a single integer programming (IP) formulation. The partition approach involves first partitioning the region geographically and solving the routing problem in each subregion with two IP formulations. Next, we strengthen the partition approach by developing a column generation (CG) approach in which the initial columns of the CG approach are the solutions generated from the partition approach. We conduct a detailed computational case study using instances based on real crime data from Montgomery County, Maryland. To further understand the computational tractability of our solution approaches, we also perform a sensitivity analysis using synthetic instances under various scenarios. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0192 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0192 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Оценка современного состояния авиалесоохранных работ на землях особо охраняемых природных территорий федерального значения

The assessment of air protection works on the lands of specially protected natural territories is presented. The disadvantages of aviation forest monitoring are considered. The analysis of the actual fire danger of forests on the lands of specially protected natural territories is carried out. To assess the risks of 111 specially protected natural territories for the period from 2002 to 2021, a retrospective analysis of the registered thermal points in the systems of space monitoring of forest fires of the information system of remote monitoring of Rosleshoz (ISDM-Rosleshoz) and FIRMS was made. The absence of fires in certain periods does not exclude the risks of their occurrence. Based on the analysis of thermal points, the predominance of spring fires was revealed. The description of the areas characterized by the occurrence of fires depending on the season of the year is given. The result of the analysis of the situation with the occurrence of forest fires on the lands of specially protected natural territories in the context of federal districts is presented. During the study of the forest protection system on the lands of specially protected natural territories, an expert assessment of the organizational structure, forms and composition of fire extinguishing teams, as well as the availability of fire extinguishing forces and means was carried out. The structure of forest fire formations, a communication and warning system for ground and aviation forces are analyzed. An expert assessment of the effectiveness of air patrol routes on the lands of specially protected natural territories was carried out. The problem of remoteness of aircraft bases has been identified. Solutions to problems for areas with low and high burnability are proposed. The problem of the absence of an approved unified methodology for assessing the probability of landscape (natural) fires on lands of other categories, from the territory of which there may be a threat of fire transfer to the lands of specially protected natural territories, is revealed. Proposals have been developed for the formation of optimal patrol routes for manned and unmanned aircraft

Determining Accurate Patrol Routes Using Genetic Algorithm and Ant Colony

Determining Accurate Patrol Routes Using Genetic Algorithm and Ant Colony

STUDI KEPADATAN POPULASI BURUNG ENDEMIK TIMOR DI SEPANJANG JALUR PATROLI DESA FATUMNASI, CAGAR ALAM MUTIS, KABUPATEN TIMOR TENGAH SELATAN, PROVINSI NUSA TENGGARA TIMUR

Birds are wild animals that play an important role in building the food chain, helping pollination, controlling insect pest populations, and seed dispersal agents that are useful for natural forest regeneration. An animal is said to be endemic if the species is a native species that can only be found in one place and not found in other places or areas as well as Endemic Birds that will be studied in the Mutis Nature Reserve Area. This study was conducted to determine the population density of Timor endemic birds along the Patrol Route to Padang I, Fatumnasi Village, Mutis Nature Reserve, South Central Timor Regency, East Nusa Tenggara Province. This research was conducted from April to May 2022. Data collection on the Population Density of Timor's Endemic Birds used the Point Count method. Bird population density data analysis used the Krebs formula and the Eisenberg formula. The results of this study recorded that 6 species of birds with the highest population density were Myzomela Timor (0.72 ind/ha), Timor Honeysucker (0.61 ind/ha), Timor Pergam (0.55 ind/ha), Opior Timor (0.50 ind/ha) and the lowest population density or the least in the Timor Kancilan (0.24 ind/ha) and Timor Anis (0.38 ind/ha). Over time, population density will decrease or increase according to the resilience of adaptation to changes in environmental conditions and competition with other organisms.

Prevention of Motor Vehicle Theft by the Samapta Patrol (Study at the Demak Resort Police)

This study is to describe the samapta patrol units and efforts increased by the Demak Police samapta unit. This type of research uses a field of research and a qualitative approach. Data sources include primary data and secondary data with data collection techniques used are interviews, observations, and document studies Data analysis techniques used include data reduction, data presentation and conclusion drawing. Knife analysis in conducting discussions includes POAC theory and management theory. The results of the study found that curanmor crimes had fluctuated over the past three years, patrol activities carried out by the Samapta Unit of the Demak Police were carried out according to the SOP, divided into four stages, namely preparation stage, organization stage, implementation stage andevaluation/ termination stage. Some actors are inadequate and external facilities and infrastructure are the jurisdiction of the Demak Police which is quite wide and the intentions of criminals are difficult to predict. Based on the results of the study, the author suggests that in the creation of patrol routes always pay attention to the development of the last Kamtibmas situation in Kabupaten Demak. Often establish cooperation with the community, such as Hansip post, Kamra post, Security Guard post, and Kamling post selectively to facilitate monitoring locations prone to theft of motor vehicles and patrols through residential areas of the community.