Route Length Research Articles

Valley Path Planning on 3D Terrains Using NSGA-II Algorithm

Valley path planning on 3D terrains holds significant importance in navigating and understanding complex landscapes. This specialized form of path planning focuses on finding optimal routes that adhere to the natural contours of valleys within three-dimensional terrains. The significance of valley path planning lies in its ability to address specific challenges presented by valleys, such as varying depths, steep slopes, and potential obstacles. By following the natural flow of valleys, path planning can enhance the efficiency of navigation and minimize the risk of encountering difficult terrain or hazards. In recent years, an increasing number of researchers have focused on the study of valley path planning on 3D terrains. This study presents a valley path planning method utilizing the NSGA-II (Non-dominated Sorting Genetic Algorithm II) approach. To ensure that the paths generated by the algorithm closely follow the valley lines, the algorithm establishes an optimization function that includes three optimization criteria: mean altitude, flight route length, and mean offset. To test the performance of this algorithm, we conducted experiments based on workspaces based on three datasets full of 3D terrains and compared it with three baseline algorithms. The evaluation indicates that the suggested algorithm successfully designs routes that closely follow the valley contours.

Investigating the Limits of Familiarity-Based Navigation.

Insect-inspired navigation strategies have the potential to unlock robotic navigation in power-constrained scenarios, as they can function effectively with limited computational resources. One such strategy, familiarity-based navigation, has successfully navigated a robot along routes of up to 60 m using a single-layer neural network trained with an Infomax learning rule. Given the small size of the network that effectively encodes the route, here we investigate the limits of this method, challenging it to navigate longer routes, investigating the relationship between performance, view acquisition rate and dimension, network size, and robustness to noise. Our goal is both to determine the parameters at which this method operates effectively and to explore the profile with which it fails, both to inform theories of insect navigation and to improve robotic deployments. We show that effective memorization of familiar views is possible for longer routes than previously attempted, but that this length decreases for reduced input view dimensions. We also show that the ideal view acquisition rate must be increased with route length for consistent performance. We further demonstrate that computational and memory savings may be made with equivalent performance by reducing the network size-an important consideration for applicability to small, lower-power robots-and investigate the profile of memory failure, demonstrating increased confusion across the route as it extends in length. In this extension to previous work, we also investigate the form taken by the network weights as training extends and the areas of the image on which visual familiarity-based navigation most relies. Additionally, we investigate the robustness of familiarity-based navigation to view variation caused by noise.

Methods of Diversion Road Improvement of Overpasses Under Repair

During the repair of motorway junctions, it is important to minimize transportation losses by urban vehicles for the period of road works.Methodology: Factor analysis.Purpose: The aim of research is to substantiate the best diversion road of the motorway junction under repair in the streets network adjacent to the object.Research findings: The dependence is shown of the average speed of cars and transportation cost on the distance between signalized intersections and traffic load.Value: An algorithm is developed for substantiation of the diversion road for construction site. Justification for the diversion road along the streets network adjacent to the object, is developed using the factor analysis and based on the traffic load, of roadway conditions, traffic lights, route length.

Urban heterogeneity of the trade-offs between exposure to greenery and walking distance in children's home–school routes

Greenery fosters sustainable outdoor environments and promotes walking and healthy lifestyles. Greening children's home–school routes can be seen as an environmentally sustainable solution that enhances their daily interaction with nature. However, the uneven distribution of greenery and route networks within cities results in varied opportunities for pedestrians, including children, to experience greenery.This study evaluates the urban heterogeneity of trade-offs between exposure to greenery and route length. We focus on the shortest and greenest hypothetical home–school routes for all children in the city. For this purpose, we conducted a spatial analysis using multiple spatially explicit data sets on primary schoolchildren, pedestrian street networks, and high-resolution urban green space maps, with Łódź (Poland) as the case study city.Children who opt for the greenest hypothetical routes instead of the shortest could increase their exposure to greenery by 18%. However, maximizing exposure to greenery requires choosing routes 9.5% (46 m) longer than the shortest alternative. The trade-off between the shortest and greenest home–school routes is more pronounced for children in the urban core area compared to other city zones. This urban heterogeneity should be considered when allocating new greenery to support active transportation.

Searching Method for Three-Dimensional Puncture Route to Support Computed Tomography-Guided Percutaneous Puncture.

In CT-guided percutaneous punctures-an image-guided puncture method using CT images-physicians treat targets such as lung tumors, liver tumors, renal tumors, and intervertebral abscesses by inserting a puncture needle into the body from the exterior while viewing images. By recognizing two-dimensional CT images prior to a procedure, a physician determines the least invasive puncture route for the patient. Therefore, the candidate puncture route is limited to a two-dimensional region along the cross section of the human body. In this paper, we aim to construct a three-dimensional puncture space based on multiple two-dimensional CT images to search for a safer and shorter puncture route for a given patient. If all puncture routes starting from a target in the three-dimensional space were examined from all directions (the brute-force method), the processing time to derive the puncture route would be very long. We propose a more efficient method for three-dimensional puncture route selection in CT-guided percutaneous punctures. The proposed method extends the ray-tracing method, which quickly derives a line segment from a given start point to an end point on a two-dimensional plane, and applies it to three-dimensional space. During actual puncture route selection, a physician can use CT images to derive a three-dimensional puncture route that is safe for the patient and minimizes the puncture time. The main novelty is that we propose a method for deriving a three-dimensional puncture route within the allowed time in an actual puncture. The main goal is for physicians to select the puncture route they will use in the actual surgery from among the multiple three-dimensional puncture route candidates derived using the proposed method. The proposed method derives a three-dimensional puncture route within the allowed time in an actual puncture. Physicians can use the proposed method to derive a new puncture route, reducing the burden on patients and improving physician skills. In the evaluation results of a computer simulation, for a 3D CT image created by combining 170 two-dimensional CT images, the processing time for deriving the puncture route using the proposed method was approximately 59.4 s. The shortest length of the puncture route from the starting point to the target was between 20 mm and 22 mm. The search time for a three-dimensional human body consisting of 15 CT images was 4.77 s for the proposed method and 2599.0 s for a brute-force method. In a questionnaire, physicians who actually perform puncture treatments evaluated the candidate puncture routes derived by the proposed method. We confirmed that physicians could actually use these candidates as a puncture route.

Optimization of Chinese Postman Problem Using Fuzzy-Based Priority Weighted Graph

: In the early 1960s, Chinese mathematician Mei-Ko Kwan (M. Guan) introduced a method aimed at minimizing mail carriers’ route lengths. This method led to the exploration of various formulations of the Chinese Postman Problem (CPP), resulting in at least eight distinct formulations. In response to a practical concern, a new problem formulation called the Priority Constrained Chinese Postman Problem (PCCPP) emerged. In PCCPP, a linear order is provided for a set of significant nodes, and the objective is to traverse all edges at least once while prioritizing the prompt visitation of higher-priority nodes. This paper presents a modified approach to Chinese Postman Problems (CPP) using priorities in a fuzzy environment ranking function applied to priority nodes in CPP to get the optimal result. Finally, this paper also illustrates and justifies the implementation of the modified approach with a numerical problem. The consideration of multi-factors in a multi-graph with priorities gives another scope of research. JEL Codes: C44 Received: 18/07/2024. Accepted: 29/09/2024. Published: 13/10/2024.

Двоетапний метод розв’язання задачі комівояжера на основі генетичного алгоритму

One of the main tasks in logistics is to find the most efficient route in the traveling salesman problem on a given transportation network, allowing for the servicing of the maximum number of customers while considering certain criteria. In the typical traveling salesman problem, the objective function is most often the route length or duration. However, such a formulation does not account for the subjectivity in evaluating the travel durations at different stages, which may be influenced by various objective and subjective factors. The problem of the traveling salesman with fuzzily defined travel durations between network nodes is considered. The approach to solving the problem is based on a genetic algorithm with enhanced mutation procedures and population diversity generation. Trapezoidal fuzzy numbers are used to formalize fuzzy values, presented in the generalized case based on the application of Gaussian distribution and corresponding characteristics. A two-stage scheme for solving the optimization problem of the traveling salesman on a given transportation network is proposed, combining the genetic algorithm and Ward's clustering method. In the first stage, a clustering procedure is conducted using Ward's method. In the second stage, based on the obtained set of clusters and the identified optimal inter-cluster distances, route duration optimization is performed within the clusters. To test the efficiency of the developed method, two types of models were generated: one with a limited number of available paths and one with a fully connected topology. Computational experiments were conducted to apply the developed method to solving the traveling salesman problem with travel durations between cities represented as fuzzy numbers. A comparison of numerical calculation results was performed. The obtained results demonstrated the effectiveness of preliminary clustering when using the genetic algorithm to find the best local optima under the same input parameters.

Economic and environmental benefits of optimized waste transportation routes in Khulna

Urban transportation is a big source of pollution and greenhouse gas emissions. Khulna City handles the waste of 1.23 million people daily, using Heavy Duty Diesel Vehicles (HDDV) that release major environmental pollutants. The current waste transportation routes lack proper planning, leading to extra fuel consumption and pollutant emissions. This study aims to reduce fuel consumption and socio-environmental costs by improving waste transportation routes. Present collection routes, collecting points, and the speed of 30 waste collector vehicles were tracked using GPS devices for ten months between July 2023 and April 2024. The network dataset was modified according to four types of waste collector vehicles. ArcGIS Pro was used to find the optimal paths and their lengths. Fuel usage and prices were estimated using simple fuel consumption rate calculations. The lengths of current and optimal routes were used in COPERT to measure pollutant emissions. The results show that improved routes lower fuel consumption costs by approximately USD 55.39 per day and CO2 emissions by about 38.242 tonnes per year. Reductions in CO, CH4, NMVOC, NO2, PM10, and PM2.5 were roughly 9.3%, 9.53%, 9.21%, 9.29%, 9.35%, and 9.3%, respectively. This study demonstrates a comprehensive approach for improving solid waste management covering both financial efficiency and environmental effects.

ERGONOMIC AND LOGISTIC ENSURING THE REQUIREMENTS FOR THE COMFORT DURING THE ORGANIZATION OF PASSENGER TRANSPORTATION IN A SMALL-CAPACITY BUS

Purpose. The most important advantage of passenger transport by road is the high quality of transport service, which depends on the organization of the transport process, structural features and technical condition of the rolling stock used and the route, the development of the route network and other factors. Comfort reflects the properties of bus passenger transportation, which lead to the creation of the necessary service conditions and convenience for passengers in the bus, at the start-end and transit points on the basis of regulatory documents approved in the prescribed manner. Features of the design of small-capacity buses in terms of comfort and safety of passenger transportation and provision of proper travel conditions mostly determine the scope of use of buses of this class by types of connections and length of routes. The purpose of the paper is the development of a structural model, based on the analysis of the main factors affecting the determination of the comfort of a small-capacity bus for urban transportation during the transportation of passengers. Methods. The analysis of literary sources showed that indicators of comfort (as well as indicators of information service, speed, timeliness, safety of cargo, safety), which refer to indicators of the quality of passenger transportation, are decisive in this group of indicators. From the point of view of Ukrainian legislation and regulatory framework, the concepts of passenger bus capacity and comfort are analyzed. The main factors that determine the comfort of small-capacity buses for urban transportation during the transportation of passengers are determined. Ensuring the comfort of passenger transportation in a small-capacity city bus is determined by compliance with ergonomic requirements, which, in turn, depends on the design features of the arrangement of the area intended for passengers. Scientific novelty. It is proposed to evaluate the comfort category in the urban public road transport system using a structural model that combines two sets of factors – logistic and ergonomic. The logistic set of factors includes characteristics in time and space, that is, characteristics of comfort outside the means of urban public road transport. The ergonomic set of factors includes characteristics that describe comfort in vehicles. Practical significance. On the basis of this model, possible ways of ensuring ergonomic requirements for the layout of the interior and interior of small-capacity buses for urban passenger transportation are proposed. It was established that the main factors of the redistribution of transport mobility between urban passenger transport and transport for individual use are the insufficient comfort of passenger transport or the insufficient density of the route network.

Flow prediction of mountain cities arterial road network for real-time regulation

This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.

Short Rest Between Flights Is Associated With Increased Serum Stress Hormone Levels in Airline Pilots: A Cross-Sectional Study.

Work-related stress is a critical area of research in civil aviation, given the potential for severe consequences when airline pilots (APs) are overwhelmed or unable to perform optimally. While pilots are traditionally considered to be exposed to various stressors, the impact of specific occupational characteristics on stress in the aviation industry remains inadequately understood. Considering that biomarkers are increasingly being utilized as objective measures of stress in human research, this cross-sectional study investigated the association between occupational variables and serum levels of cortisol and dehydroepiandrosterone sulfate (DHEAS) as stress biomarkers in commercial APs. A total of 120 male APs completed a survey assessing the following work-related characteristics: position (captain vs. first officer), years of experience, total flight hours, flight hours in the preceding year, inter-flight rest duration, and flight route length. Serum cortisol and DHEAS concentrations were determined from venous blood samples obtained between 08:00 and 09:00 a.m., following a minimum eight-hour fasting period. The biomarker data were analyzed in relation to the aforementioned occupational characteristics. The mean serum cortisol and DHEAS concentrations in the entire cohort were 8.5±2.1 µg/dL and 214.6±96.4 µg/dL, respectively. There were no significant differences in the levels of the two stress hormones in relation to position, years of experience, total flight hours, flight hours in the preceding year, or flight route length. However, an inter-flight rest period of less than one hour was significantly associated with elevated serum cortisol (P<0.01) and reduced DHEAS levels (P<0.001) compared to longer rest durations. Notably, a stepwise decrease in DHEAS concentrations was observed for rest periods of <1 hour, 1-4 hours, 4-24 hours, and >24 hours. After adjustment for potential confounders in multivariable analyses, a rest period of <1 hour remained independently associated with both serum cortisol (odds ratio [OR]=1.09, 95% confidence interval [CI]=1.04-1.13, P<0.01) and DHEAS (OR=0.94, 95% CI=0.92-0.97, P<0.001). Serum stress hormones in APs are associated with short inter-flight rest periods. Optimization of rest durations may contribute to improved pilot well-being and performance. Further research is warranted to determine ideal rest period lengths and develop interventions to mitigate the potential adverse effects of abbreviated rest periods between flights.

New Energy Electric Vehicle Industry Development Trends Forecast

This study focuses on developing the MLPRegressor model for predicting the future trends in China's new energy electric vehicle industry over the next decade. We employed a dataset comprising 16,500 records, encompassing 12 features, such as the number of public electric vehicles, the length of electric vehicle operational routes, and the number of electric vehicles owned by residents, for model training. We compared our model with traditional models like ARIMA, XGBRegressor, and Random Forest Regressor, using MAPE and MSLE as evaluation metrics. The final results revealed that our model exhibited superior performance in terms of loss, outperforming the other models significantly. This research not only provides businesses with more accurate strategies for developing the new energy electric vehicle industry but also serves as a crucial reference for the government in formulating environmentally friendly policies.

FORMATION OF A NETWORK OF EVACUATION BUS ROUTES FOR THE SALTIV RESIDENTIAL AREA OF KHARKIV

Evacuation mobility of the population during military operations or emergencies is one of the most vital tasks in ensuring the safety and rescue of citizens. Planning this mobility requires a conceptual approach, system analysis, and a comprehensive preparatory stage. The primary task for organising the population’s bus transportation during evacuation is planning routes and determining their time parameters. This process requires preliminary determination of the points of traffic organisation, establishment of routes and meeting points, allocation of resources, and coordination of work with other types of passenger transport. The study aims to develop the sequence of formation of evacuation bus routes in the Saltiv residential area of Kharkiv and to establish the functional effect of operational parameters of their work on the duration of population evacuation. The methodological basis for building a network of evacuation bus routes is a developed structural model of the impact of input parameters on the general evacuation time for the population and the transport capabilities of the routes. This model describes the functional relationship between the existing volume of passenger transportation on urban transport routes, the share of passengers subject to evacuation, the number of stopping points on evacuation routes, the length of routes, the speed of movement, the time of boarding and disembarking the passengers, the capacity of buses and their number. The criterion for evaluating the efficiency of the routes is the total time of population evacuation, depending on the number of buses on the routes. Based on the analysis of the location of shelter facilities and transfer stations on the subway line, we established that it is expedient to create seven evacuation routes that ensure the timely evacuation of the population. After assessing the available passenger traffic on city transport routes, we determined the potential demand for evacuation transportation. The analysis of the operational parameters of the routes made it possible to specify the time indicators for the evacuation of the population. The analysis of the interaction processes resulted in a diagram of the structural contour of the functional connection. The developed analytical models helped to establish several indicators characterising the operation of evacuation bus routes. The developed theoretical foundations are the basis for simulation modelling and setting the regularity of changes in evacuation efficiency assessment indicators depending on the number and capacity of buses. Based on the real-life conditions, the study determined the need to use two buses on each route to evacuate the population in the district. Such conditions will ensure the transfer of the population to the safe zones within 18.3 to 31.7 hours. Keywords: population evacuation, bus, evacuation route, volume of transportation, travel time.

Optimization of Hub-Based Milkrun Supply

Background: Milkrun-based material supply plays an important role in the automotive industry, as it is a material supply concept where high efficiency can be achieved. When implementing milkrun-based material supply, the milkrun supply of the production plant often has to be integrated with an existing warehouse material handling system, which frequently leads to a less efficient solution. Methods: In this paper, the author investigates the impact of a hub-based milkrun supply, where the collection processes in the component’s warehouse and the distribution processes in the assembly plant are connected to a hub, which is responsible for the sequencing of component demands. After a systematic literature review, the paper introduces a novel mathematical model, which makes it possible to describe the conventional milkrun-based solutions, the hub-based milkrun solutions, and to compare them in terms of the length of transportation routes, transportation time, total service time, and virtual emission points of view. Results: The scenario analysis demonstrates that the hub-based solution can lead to an efficiency improvement of about 13% in total service time, 23% savings in transportation time, and 45% savings in transportation time in the component’s warehouse. Conclusions: The article’s findings suggest that implementing a hub-based milkrun system in automotive material supply can significantly enhance efficiency. The described approach could lead to more streamlined operations in production plants by optimizing the integration of milkrun systems.

Improved LPAstar algorithm for power station cabinet wiring

Abstract Aiming at the complex constraints of cable layout in power plant cabinets and the low efficiency of manual design, an improved Lifelong Planning Astar algorithm (LPAstar) is proposed. By constructing a corner-length feature fusion model, the cable length and number of corners are constrained to reduce wiring costs and cable damage. The spatial minimum bounding box algorithm is employed to avoid cable suspension. Continuous incentive factors are introduced to improve algorithm efficiency. In post-processing, smoothing strategy and wire harness dispersion strategy are used to avoid excessive cable bending and ensure the standardization of wiring. The simulation experiment results show that compared with the traditional Astar algorithm, the routing length and number of corners are reduced by 27% and 65%, respectively. The generation efficiency is 84% and 87% higher than the Astar algorithm and the original LPAstar algorithm, respectively.

UAV data link anti-interference via SLHS-SVM-AdaBoost algorithm: Classification prediction and route planning

The ability to predict the anti-interference communications performance of unmanned aerial vehicle (UAV) data links is critical for intelligent route planning of UAVs in real combat scenarios. Previous research in this area has encountered several limitations: Classifiers exhibit low training efficiency, their precision is notably reduced when dealing with imbalanced samples, and they cannot be applied to the condition where the UAV’s flight altitude and the antenna bearing vary. This paper proposes the sequential Latin hypercube sampling (SLHS)-support vector machine (SVM)-AdaBoost algorithm, which enhances the training efficiency of the base classifier and circumvents local optima during the search process through SLHS optimization. Additionally, it mitigates the bottleneck of sample imbalance by adjusting the sample weight distribution using the AdaBoost algorithm. Through comparison, the modeling efficiency, prediction accuracy on the test set, and macro-averaged values of Precision, Recall, and F1-score for SLHS-SVM-AdaBoost are improved by 22.7%, 5.7%, 36.0%, 25.0%, and 34.2%, respectively, compared with Grid-SVM. Additionally, these values are improved by 22.2%, 2.1%, 11.3%, 2.8%, and 7.4%, respectively, compared with particle swarm optimization (PSO)-SVM-AdaBoost. Combining Latin hypercube sampling with the SLHS-SVM-AdaBoost algorithm, the classification prediction model of anti-interference performance of UAV data links, which took factors like three-dimensional space position of UAV and antenna bearing into consideration, is established and used to assess the safety of the classical flying path and optimize the flying route. It was found that the risk of loss of communications could not be completely avoided by adjusting the flying altitude based on the classical path, and that intelligent path planning based on the classification prediction model of anti-interference performance can realize complete avoidance of being interfered while reducing the route length by at least 2.3%, thus benefiting both safety and operation efficiency.

Anatomical navigational difficulties in transradial right carotid artery stenting with 7-F Optimo balloon guide catheter.

Carotid artery stenting (CAS) using a 7-F Optimo balloon guide catheter (BGC) via the right radial artery (RA) was performed for stenosis of the right carotid artery. The factors affecting the difficulty in navigating the BGC from the right RA to the right common carotid artery (CCA) were investigated. For 40 cases of stenosis of the right carotid artery, CAS using a 7-F Optimo BGC via the right RA was performed. Pre-operative anatomical length and angle of the access route were retrospectively examined. The 7-F Optimo BGC successfully reached all lesions; however, navigational difficulties were encountered in seven out of 40 cases (17.5%). One case in the difficult group experienced an ischemic complication. The height from the topmost point of the subclavian artery (SA) to the origin of the SA (SA height) was 44.4 mm versus 28.1 mm (p < 0.01), and the angle between the SA and the CCA (SA-CCA angle) was 21.6° versus 47.9° (p < 0.01) in the difficult and easy groups, respectively. For lesions with difficult navigation, the sensitivity and specificity of the SA height >34 mm were 100% and 82%, and the sensitivity and specificity of the SA-CCA angle <30° were 100% and 82%. For stenosis of the right carotid artery, transradial-CAS using a 7-F Optimo BGC is a safe procedure. However, navigating the BGC becomes difficult when the SA height is >34 mm and the SA-CCA angle is <30°.

Penerapan Algoritma Tabu Search pada Capacitated Vehicle Routing Problem Pengangkutan Sampah di Kota Padang Sidempuan

Waste transportation activities are an example of a form of Capacitated Vehicle Routing Problem (CVRP) related to finding the minimum route. The Tabu Search algorithm is one of the metaheuristic methods that can guide the heuristic local search procedure to explore the solution area outside the local optimal point. The Tabu Search algorithm can be used to find the optimal VRP solution, namely the route that has the minimum total mileage by considering vehicle capacity. The purpose of this research is to determine the optimal route for garbage transportation in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm. Based on the results of the study, it is concluded that the optimal route for transporting waste in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm obtained the shortest route in iteration 1 with the route (12-11-10-9-8-7-6-5-4-3-2-1-0) and route length 16.55 km.

MPC-based motion control of AGV with improved A* and artificial potential field

Nowadays, automatic guided vehicles (AGV) are extensively utilized for transportation and inspection tasks in workshops. The A* and artificial potential field (APF) are classic algorithms employed for path planning of AGVs. However, these algorithms still fail to meet the actual production needs and cannot avoid stuttering while encountering obstacles, leading to excessive energy consumption and unnecessary pause. In the paper, an improved A* algorithm is proposed to reduce route length and improving efficiency. On this basis, an integrated fusion strategy consisting of improved APF and nonlinear model predictive control (NMPC) is designed for collision avoidance and path tracking control. The proposed algorithm is tested both on simulation and a laser-guided real automatic guided vehicle experimental platform. Experimental results prove that the proposed algorithm has a great tracking performance under complex workplace.

Research on escape route planning analysis in forest fire scenes based on the improved A* algorithm

The forest fire environment is complex and volatile, posing a serious threat to the lives of firefighters at any time. Aiming at how people choose escape routes when facing these dangers, this paper proposed an escape route planning method based on the improved A* algorithm. Taking the forest fire that occurred in Xintian County, Yongzhou City, Hunan Province on October 17, 2022, as the research object, this study collected data from multi-temporal remote sensing imagery including GF-4, and Sentinel-2 to obtain 11 factors affecting the escape route planning. We used the FAHP-CRITIC combination weighting method to analyze the weights of the escape impact factors. Forest fire scenes were categorized into five classes based on escape risk coefficient from high to low. The heuristic function and weight coefficients of the traditional A* algorithm were reconstructed to obtain the improved A* algorithm. The computing time of both A* algorithm is similar in the same fire scene. But the percentage of the escape route length, located inside the high-risk zones, in the total escape route planned by the improved algorithm decreased by 53.63% than that planned by the traditional A* algorithm. The escape risk coefficient was reduced by 24.22%, and the escape safety was significantly improved. On this basis, the study combined the improved A* algorithm with the dynamic escape window method to search for the nearest safe area to the escapees and compute the corresponding escape routes. The real-time safety of the algorithm was verified using GF-1 remote sensing images obtained 7 and 35 min after the previous fire burning moment. Results revealed that the escape paths planned by the improved A* algorithm remained in a safe state and were able to be updated in real time according to the trend of the fire. This demonstrates the ability of the improved A* algorithm to adapt to real-time changes in forest fire scenes. It can provide multiple reliable escape options for escapees, thus scientifically and effectively reducing human casualties.