Travel Distance Research Articles

Predictive air combat decision model with segmented reward allocation

In air combat missions, unmanned combat aerial vehicles (UCAVs) must take strategic actions to establish combat advantages, enabling effective tracking and attacking of enemy UCAVs. Currently, a lot of reinforcement learning algorithms are applied to the air combat mission of unmanned fighter aircraft. However, most algorithms can only select policies based on the current state of both sides. This leads to the inability to effectively track and attack when the enemy performs large angle maneuvering. Additionally, these algorithms cannot adapt to different situations, resulting in the unmanned fighter aircraft being at a disadvantage in some cases. To solve these problems, this paper proposes predictive air combat decision model with segmented reward allocation for air combat tracking and attacking. On the basis of the air combat environment, we propose the prediction soft actor-critic (Pre-SAC) algorithm, which combines the prediction of enemy states with the states of UCAV for model training. This enables the UCAV to predict the next move of the enemy UCAV in advance and establish a greater air combat advantage for us. Furthermore, by adopting a segmented reward allocation model and combining it with the Pre-SAC algorithm, we propose the segmented reward allocation soft actor-critic (Sra-SAC) algorithm, which solves the problem of UCAVs being unable to adapt to different situations. The results show that the prediction-based segmented reward allocation the Sra-SAC algorithm outperforms the traditional soft actor-critic (SAC) algorithm in terms of overall reward, travel distance, and relative advantage.

Multi-Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm

Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban bus route optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, and accessibility while minimizing total travel distance. H-RL-VaNSAS is evaluated against leading optimization methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), and Salp Swarm Algorithm (SSA). Using metrics such as Hypervolume and the Average Ratio of Pareto Optimal Solutions, H-RL-VaNSAS demonstrates superior performance. Specifically, H-RL-VaNSAS achieved the highest resilience index (550), sustainability index (370), safety score (480), tourist preferences score (300), and accessibility score (2300), while minimizing total travel distance to 950 km. Compared to other methods, H-RL-VaNSAS improved resilience by 12.24–17.02%, sustainability by 5.71–12.12%, safety by 4.35–9.09%, tourist preferences by 7.14–13.21%, accessibility by 4.55–9.52%, and reduced travel distance by 9.52–17.39%. This research offers a framework for designing efficient, resilient, and sustainable public transit systems that align with urban planning and transportation goals. The integration of reinforcement learning with VaNSAS significantly enhances optimization capabilities, providing a valuable tool for mathematical and urban transportation research communities.

Assessing the spatial distribution and accessibility of public and private schools in Qatar: A GIS-based analysis

Educational services are essential to the development and well-being of any city, acting as a cornerstone for individual and community advancement. The aim of this study is to analyze the spatial distribution and accessibility of public and private schools in Qatar, using various GIS tools to inform educational planning and policy. The methods employed include Kernel Density Analysis to visualize the concentration of schools, Nearest Neighbor Analysis to assess spatial patterns, Ripley’s K-function to evaluate clustering across different scales, Location Quotient to determine relative school concentrations, and Buffer Analysis to examine proximity to various land uses and hazards. Additionally, Accessibility Analysis was conducted to calculate travel times and distances to schools. The results reveal significant clustering of both public and private schools in urban centers, particularly Doha, with notable disparities in school accessibility between urban and rural areas. Policy implications highlight the need for strategic placement of new schools, improvement of existing facilities, and targeted interventions in underserved regions to ensure equitable access to quality education for all students across Qatar.

School choice with independent versus consolidated districts

This paper studies the welfare effects of school district consolidation. Using incomplete rank-ordered lists (ROLs) submitted for admission to the Hungarian secondary school system, we estimate complete ROLs assuming that parents do not use dominated strategies and that the matching outcome is stable. These estimates aid in constructing a counterfactual district-based assignment and discerning the factors driving parents' preferences over schools. We find that district consolidation leads to large welfare gains in Budapest, equivalent to students attending a school five kilometres closer to their residences. These gains offset the additional travel distances incurred in the consolidated assignment. 73% of matched students benefit from district consolidation, while fewer than 3% are assigned to a less preferred school. Students from smaller and less under-demanded districts benefit relatively more, as well as those with high academic ability. Using reported preferences instead of estimated ones also yields large gains from district consolidation.

Using mobile phone data to capture residential segregation and its association with travel mobility

AbstractThis study measures residential segregation and investigates its association with travel mobility by using mobile phone data from Shenzhen, China. It considers residential segregation in terms of income level and migrant group; and travel mobility in terms of travel frequency, activity space, and travel distance. Unlike previous research on residential segregation and travel mobility, our research uses mobile phone data to produce empirical evidence. The extent of segregation between different migrant and income‐level groups is measured using the location quotient and Getis‐Ord index. This enables us to develop a linear regression model with which to investigate the associations of residential segregation with travel mobility. The study results show that the segregation of middle‐ and low‐income groups and migrants from Southwestern China and Jiangxi is negatively associated with travel mobility among those in the suburbs; meanwhile, for groups segregated in the city center, there is a positive association with travel mobility. These findings suggest that residential segregation is especially adverse for the travel mobility of disadvantaged groups and those living in the suburbs. Accordingly, the paper presents policy recommendations that would enhance travel mobility by alleviating the problems associated with the residential segregation of socially disadvantaged groups living in the suburbs.

Sistem Monitoring Berbasis IoT pada Pembangkit Listrik Tenaga Mikrohidro Tangsijaya

The Micro Hydro Power Plant of Gunung Halu was established in 2007. While electricity from the national power grid is available in the village of Gunung Halu, not all hamlets receive a consistent power supply, particularly those with hilly topography and areas surrounded by forests like Dusun Tangsijaya. The existence of the micro hydro power plant in Tangsijaya is supported by a stable water flow from the river. However, one of the challenges faced by the operator is the lack of a monitoring system for the micro hydro power generator that can provide information about its condition, such as power output and electric current. Until now, the operator has checked the data by visiting the location of the micro hydro power plant directly, which is located outside the residential area. Manual recording relies on the existence of human resources and produces results with a low level of accuracy. In this community service activity, a monitoring system was installed at the Tangsijaya's micro hydro power plant. Leveraging Internet of Things (IoT) technology, this system can display data in real time, so that data visualization can be monitored by relevant parties. With a monitoring system, micro-hydro operators can control the usage and distribution of the generated electricity. In addition to obtaining records of daily electricity usage data, recorded data provide very valuable information in assessing current and potential electricity usage for making decisions related to electricity production. After the implementation of the monitoring system, the main benefits are felt by microhydro operators where the work processes they do become more efficient. Efficiency is obtained in terms of travel distance where operators do not need to visit the PLTMH location directly, and also efficiency in the recording process which no longer needs to be done manually.

Spatio-temporal task pricing for shared electric micro-mobility battery-swapping platform with reinforcement learning

Spatial crowdsourcing has emerged in shared electric micro-mobility platforms, compensating occasional drivers (ODs) per task of swapping micro-mobility batteries. As ODs autonomously select tasks only when satisfied with predetermined compensation and travel distance, a traditional uniform pricing strategy results in possible low task completion. To resolve the imbalance between ODs and tasks, this study introduces a spatio-temporal pricing strategy where task prices differ by region and time interval. Considering the daily variations in task distribution and OD availability, the goal is to minimise the platform costs equal to the sum of total OD wages and penalties for uncompleted tasks. The reinforcement learning approach with proximal policy optimisation (PPO) is implemented to generate real-time continuous task prices. A domain-specific masking technique is incorporated to improve the learning process by disregarding the data from inactive grids in loss calculations. Computational results show that the PPO agent strategically raises prices in regions with insufficient ODs according to the OD density level. Further comparison with the mixed integer programming model with perfect information on ODs' willingness-to-accept parameters demonstrates the superior capability of our algorithm in navigating the uncertainties of OD task acceptance. A sensitivity analysis provides insights into the decision of system parameters.

Picker routing in scattered storage warehouses: an evaluation of solution methods based on TSP transformations

AbstractTo optimize their order fulfillment processes, many e-commerce warehouses employ a storage assignment strategy known as scattered or mixed-shelves storage. Under this approach, unit loads of homogeneous products are divided, and individual pieces are stored in various shelves throughout the warehouse. This arrangement ensures that products that appear together on unpredictable pick lists are stored in close proximity somewhere in the huge warehouses, reducing the travel distance for pickers. Despite these advancements, efficiently guiding pickers through the warehouse remains a significant planning challenge. Since the same products can be found in multiple storage positions, the traditional picker routing problem becomes more complex, as an additional selection task arises regarding which shelf to retrieve each requested product from. While previous research has developed several tailor-made solution algorithms, we demonstrate that known transformation schemes used for different variants of the well-known Traveling Salesman Problem (TSP) can be utilized to convert the single picker routing problem with scattered storage (SPRP-SS) into a classical TSP. This approach enables us to leverage the extensive array of state-of-the-art TSP solvers. The purpose of this paper is to explore the performance of these solvers when applied to solving the SPRP-SS. Through our computational study, we found that existing TSP solvers exhibit good performance, allowing near-optimal solutions to be obtained in less than a second for real-world scale SPRP-SS instances. Moreover, the efficiency of these TSP solvers remains unaffected by the number of cross aisles in the warehouse. Consequently, we exploit this flexibility to investigate the impact of cross aisles on picking performance in scattered storage warehouses.

Dental care access for children in the United States.

To evaluate access to dental care for children in the United States. The study population included children in 48 states and the District of Columbia. Using multiple data sources, dental care access was estimated at the community level by matching dental care supply and demand using mathematical modeling accounting for access constraints. Outcome measures included percent-met demand, travel distance, and percentage of underserved and unserved communities. Multiple scenarios to improve Medicaid/CHIP participation of dentists were evaluated. Medicaid-insured and CHIP-insured children exhibited lower access compared to those privately insured. The percent-met demand was lower than 50% for Medicaid-insured children and CHIP-insured children for 42 and 34 states, respectively. Percent-met demand was higher than 50% for private-insured children except for Texas and West Virginia. Increasing Medicaid/CHIP participation of dentists resulted in improving access for public-insured children. At 100% Medicaid/CHIP participation, all states exhibited different degrees of percent-met demand increase for publicly insured children, from 7% to 46%. The percent-met demand across all children ranged in 23.8%-82.9% under 70% participation rate versus 22%-83% under 100% participation rate. No single participation rate improved access for all children uniformly across all states. This study found that dental care access was lower for children with public insurance than those with private access across all states, although states responded differently to changes in Medicaid/CHIP participation. Increasing access for children with public insurance would reduce disparities, but overall children's access to dental care would be better improved by expanding the oral health workforce.

Optimization of 3D Printing While Traveling En Route to Extend Range of Unmanned Aircraft Systems for Multilocation Mission Scenarios

Abstract The nexus of two relatively recent technologies, additive manufacturing and unmanned aircraft systems (UAS), has enabled new and unique capabilities that have only started to be realized in integrated systems. This article explores and quantifies the impact of 3D printing parts for UAS, or entire UAS systems, on an agent platform, while this agent travels to multiple locations as part of a mission objective. The fully printed or enhanced UAS can then be released at launch points farther away from the goal locations. This, in turn, can accelerate mission completion times and reduce travel costs depending upon the ratio between vehicle speed and 3D printing rate. Thousands of scenarios are optimized across the design space to minimize the travel path length for the agent platform as a result of 3D printing en route to the locations of interest. Results indicate that based on the print capability and agent travel speed, an exponential decay in the amount of travel distance of the agent platform occurs. For unity ratios of print speed and agent speed in the considered design space, a decrease of 55% in the total required distance of our agent is observed. This reduction in total travel distance can reduce time, fuel, cost, and other aspects including other environmental and social impacts. A generalized optimization formulation is also presented at the end to enable similar analyses with other en route range-extending technology such as battery charging.

Barriers and Attitudes towards Mental Health Services in Nigeria: A Systematic Review

Mental health services in Nigeria consists mainly of large government psychiatric hospitals. There are eight Neuropsychiatric hospitals and a similar number of teaching hospital psychiatric departments for a population of 200 million people. There is only one private community residential facility available with ten beds in Lagos State and it is administered by a religious organization for rehabilitation of persons with drug problems.In Nigeria, an estimated 20-30%of the population are believed to suffer from mental disorders.MEDLINE EntrezPubmed search was done in November, 2018. Studies conducted on mental health and mental health services in Nigeria done from 2009 till date were selected for review. The studies were grouped into two categories according to the key words used in the search. Category 1: Keywords used in the search: mental health,Nigeria.This search yielded a total of one thousand and fifty (1,051) publications. Category 2: Keywords used in the search: mental health services,Nigeria. Lack of incentives for health care workers and inadequate workforce were identified as some of the barriers against the use of mental health services in Nigeria. Other major barriers identified were- poor education, ignorance, and stigmatization. Absence of services in rural communities, waiting time at the facilities, bureaucracy in treatment and poor information management, high cost of service, travel distance, feelings of shame and loss of productive income were the barriers identified in some other studies conducted in Nigeria.

Municipal street‐sweeping area generation with route optimization

AbstractStreet sweeping is used in urban municipalities worldwide but requires a great deal of planning for large‐scale implementation. Municipalities typically make use of the macro‐ and microapproach by using operational areas in which routes are assigned, but creating operational areas without an understanding of the expected workload, number of routes, and the travel distance to and from the depot may lead to increased statistics. In this paper, a combination of heuristic approaches is used to assign street‐sweeping areas on the macroscale and generate street‐sweeping routes on the microscale. For the area assignment, a two‐stage cluster approach is proposed, making use of the weighted k‐means algorithm and differential evolution. For the route optimization, a three‐phase augment merge algorithm is used to create initial solutions, the u‐turns are minimized with a modified version of Hierholzer's algorithm and tabu search, and the remaining u‐turns are removed using a forward‐searching ant colony optimization. A case study in The City of Oshawa, Canada, was used to verify the proposed methodology, and all metrics were theoretically improved.

Views and opinions of patients with glaucoma and age-related macular degeneration on vision home-monitoring: a UK-based focus group study

ObjectiveTo investigate the views, hopes and concerns of patients living with glaucoma and age-related macular degeneration (AMD) regarding vision home-monitoring.DesignQualitative study using focus groups and questionnaires. Participants were given three...

Spatial Analysis of Advanced Air Mobility in Rural Healthcare Logistics

The transportation of patients in emergency medical situations, particularly in rural areas, often faces significant challenges due to long travel distances and limited access to healthcare facilities. These challenges can result in critical delays in medical care, adversely affecting patient outcomes. Addressing this issue is essential for improving survival rates and health outcomes in underserved regions. This study explored the potential of advanced air mobility to enhance emergency medical services by reducing patient transport times through the strategic placement of vertiports. Using North Dakota as a case study, the research developed a GIS-based optimization workflow to identify optimal vertiport locations that maximize time savings. The study highlighted the benefits of strategic vertiport placement at existing airports and hospital heliports to minimize community disruption and leverage underutilized infrastructure. A key finding was that the optimized mixed-mode routes could reduce patient transport times by up to 21.8 min compared with drive-only routes, significantly impacting emergency response efficiency. Additionally, the study revealed that more than 45% of the populated areas experienced reduced ground travel times due to the integration of vertiports, highlighting the strategic importance of vertiport placement in optimizing emergency medical services. The research also demonstrated the replicability of the GIS-based optimization model for other regions, offering valuable insights for policymakers and stakeholders in enhancing EMS through advanced air mobility solutions.

Optimizing the location of vaccination sites to stop a zoonotic epidemic

Mass vaccinations are crucial public health interventions for curbing infectious diseases. Canine rabies control relies on mass dog vaccination campaigns (MDVCs) that are held annually across the globe. Dog owners must bring their pets to fixed vaccination sites, but sometimes target coverage is not achieved due to low participation. Travel distance to vaccination sites is an important barrier to participation. We aimed to increase MDVC participation in silico by optimally placing fixed-point vaccination locations. We quantified participation probability based on walking distance to the nearest vaccination site using regression models fit to participation data collected over 4 years. We used computational recursive interchange techniques to optimally place fixed-point vaccination sites and compared predicted participation with these optimally placed vaccination sites to actual locations used in previous campaigns. Algorithms that minimized average walking distance or maximized expected participation provided the best solutions. Optimal vaccination placement is expected to increase participation by 7% and improve spatial evenness of coverage, resulting in fewer under-vaccinated pockets. However, unevenness in workload across sites remained. Our data-driven algorithm optimally places limited resources to increase overall vaccination participation and equity. Field evaluations are essential to assess effectiveness and evaluate potentially longer waiting queues resulting from increased participation.

Access to clinical genetic services: An evaluation of patient referral characteristics and identifying barriers in Michigan.

The utilization of genetics in medical care has enhanced the utility of precision medicine and hence increased the need for clinical genetic services. These services have reduced the costs and expanded the availability of genetic testing, but their use is limited in certain populations. This study explores the access to clinical genetic services for Michigan patients referred to a genetics clinic on the western side of the state. Factors included the travel distance (miles), wait time for appointment (days from the referral date to the date of first appointment), population demographics, and cultural characteristics. A retrospective record review of all aged patients (n = 568) referred to a genetics clinic in 2018 demonstrated that all patients were insured (100%), of which majority were white-non-Hispanic (90.7%), more than half were < 10 years of age at referral (53.3%), and most of them kept their first appointment (93.5%). Our analysis showed that the wait time was associated with referral non-compliance, p < 0.01. Adjusting for all variables, for each additional day in wait time, patients had 1% increased risk of not seeking clinical genetic services (OR = 1.01, 90% CI [1.01, 1.02]). Policies to encourage genetic service utilization and improve equitable access to precision health are needed. An opportunity exists for strategies that broaden and add diverse populations to those receiving genetic services.

The association of travel burden with prenatal care utilization, what happens after provider-selection

BackgroundBirthing people in the United States face numerous challenges when accessing adequate prenatal care (PNC), with transportation being a significant obstacle. Nevertheless, previous studies that relied solely on the distance to the nearest provider cannot differentiate the effects of travel burden on provider selection and care utilization. These may exaggerate the degree of inequality in access and fail to capture perceived travel burden. This study investigated whether travel distances to the initially visited provider, to the predominant PNC provider, and perceived travel burden (measured by the travel disadvantage index (TDI)) are associated with PNC utilization.MethodsA retrospective cohort of people with live births were identified from South Carolina Medicaid claims files in 2015–2018. Travel distances were calculated using Google Maps. The estimated TDI was derived from local pilot survey data. PNC utilization was measured by PNC initiation and frequency. Repeated measure logistic regression test was utilized for categorical variables and one-way repeated measures ANOVA for continuous variables. Unadjusted and adjusted ordinal logistic regressions with repeated measure were utilized to examine the association of travel burdens with PNC usage.ResultsFor 25,801 pregnancies among those continuously enrolled in Medicaid, birthing people traveled an average of 24.9 and 24.2 miles to their initial and predominant provider, respectively, with an average TDI of -11.4 (SD, 8.5). Of these pregnancies, 60% initiated PNC in the first trimester, with an average of 8 total visits. Compared to the specialties of initial providers, predominant providers were more likely to be OBGYN-related specialists (81.6% vs. 87.9%, p < .001) and midwives (3.5% vs. 4.3%, p < .001). Multiple regression analysis revealed that every doubling of travel distance was associated with less likelihood to initiate timely PNC (OR: 0.95, p < .001) and a lower visit frequency (OR: 0.85, p < .001), and every doubling of TDI was associated with less likelihood to initiate timely PNC (OR: 0.94, p = .04).ConclusionsFindings suggest that the association between travel burden and PNC utilization was statistically significant but of limited practical significance.

Nonlinear Relationship of Multi-Source Land Use Features with Temporal Travel Distances at Subway Station Level: Empirical Study from Xi’an City

The operation of the subway system necessitates a comprehensive understanding of passenger flow characteristics at station locations, as well as a keen awareness of the average travel distances at these stations. Moreover, the travel distances at the station level bear a direct relationship with the built environment composed of land use characteristics within the station’s catchment area. To this end, we selected the land use features within an 800 m radius of the station (land use area, distribution of points of interest, and the surrounding living environment) as the influencing factors, with the travel distances at peak hours on the subway network in Xi’an as the research subject. An improved SSA-XGBOOST-SHAP interpretable machine learning framework was established. The research findings demonstrate that the proposed enhanced model outperforms traditional machine learning or linear regression methods in terms of R-squared, MAE, and RMSE. Furthermore, the distance from the city center, road network density, the number of public transit routes, and the land use mix have a pronounced influence on travel distances, reflecting the significant impact that mature built environments can have on passenger attraction. Additionally, the analysis reveals a notable nonlinear relationship and threshold effect between the built environment variables comprising land use and the travel distances during peak hours. The research results provide data-driven support for operational strategy management and line capacity optimization, as well as theoretical underpinnings for enhancing the efficiency and sustainability of the entire subway system.

Efficiency enhancement in waste management through GIS-based route optimization

Due to rapid urbanization and uncontrolled population growth, solid waste management has become a rising issue in developing nations like Bangladesh. Khulna ranks third among Bangladesh's metropolitan cities. The existing system of Khulna City for transporting municipal solid waste lacks route planning and depends exclusively on the driver's expertise. Hence, this study aims to minimize the length of the present travel paths and assess the advantages of using optimized routes. Secondary datasets like a list of vehicles, financial information, and a list of secondary transfer stations have been collected from the municipality. A field survey was conducted to capture the coordinates of secondary transfer stations. Multiple GPS devices have been installed for monitoring the current paths, stops, and travel time of 31 waste collector vehicles for 10 months. A network database has been prepared and modified by integrating driver's perceptions and field inspection. Network analysis of ArcGIS Pro has been used for finding the optimized routes. The optimized route minimizes the travel distance by 9.40%. By following the optimized routes, about 11.6% of fuel costs can be reduced. This research has the potential to make a significant contribution to the waste management of Khulna City and beyond.

Multi-strategy cooperative scheduling for airport specialized vehicles based on digital twins

Efficient specialized vehicle cooperative scheduling is significant for airport operations, particularly during times of high traffic, which reduces the risk of flight delays and increases customer satisfaction. In this paper,we construct a multi-type vehicles collaborative scheduling model with the objectives of minimizing vehicle travel distance and vehicle waiting time. Additionally, a three-layer genetic algorithm is designed, and the crossover and mutation operations are enhanced to address the scheduling model. Due to the numerous uncertainties and stochastic interferences in airport operations, conventional scheduling methods unable to effectively address these challenges, this paper combines improved genetic algorithm, simulation algorithm, and digital twins technology, proposing a multi-strategy scheduling framework for specialized vehicles based on digital twins. The scheduling framework utilises digital twins to capture dynamic data from the airport and continuously adjusts the scheduling plan through the scheduling strategy to ensure robust scheduling for specialized vehicles. In the event of severe delays at the airport, fast and efficient re-scheduling can be achieved. Finally, the effectiveness of the proposed scheduling framework is validated using domestic flight data, and extensive experiments and analyses are conducted in different scenarios. This research contributes to addressing the optimization problem of cooperative scheduling for multi-type vehicles at airports.