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Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forest roads have been recognized as one of the significant contributors to soil erosion processes in forested areas. The construction and maintenance of forest roads can cause severe environmental impacts, including soil erosion, sedimentation, and degradation of aquatic ecosystems. The main objective of the present study is to analyze the impact of forest road networks on soil erosion and sedimentation in the context of the Zagros forestlands, Iran. This study aims to assess the soil erosion and sedimentation on forest roads in four case studies in the Zagros forestlands. This study collected data using field surveys and SEDMODL equations to determine input factors and sedimentation and erosion rates. This study found that roadside erosion is strongly correlated with geological factors, road width, and precipitation factors. The height changes of 144 benchmarks were recorded during one study year (2021–2022) on four study roads, and the measured results of erosion benchmarks indicated an average soil erosion of 3, 2.6, 4.7, and 3.5 mm per year around the Bideleh, Kohian, Nazi, and Tabarak roads, respectively. This study measured soil erosion and sedimentation at three distances (5, 15, and 25 m) from the road, and found a significant difference in the height changes of the benchmarks at varying distances from the study roads. A hot-spot analysis was conducted using GIS 10.8, and the results indicated that a significant portion of the studied forest roads had very high erosion production and hot spots. The results of the hot-spot analysis indicated that 30.8%, 22.6%, 39.8%, and 14.5% of the study forest roads, Nazi, Tabarak, Bideleh, and Kohian roads, respectively, are identified as areas with very high erosion production and hot spots. These results highlight the need for effective management strategies to minimize the impact of erosion on road infrastructure and the surrounding environment. Overall, this study provides important insights into the soil erosion and sedimentation on forest roads, and the findings presented here can be used to inform future road construction and maintenance. [ABSTRACT FROM AUTHOR]

The Garhwal and Kumaun regions of the Himalayas of India have experienced rapid urbanisation due to the expansion of the national highway (NH-58) in Uttarakhand, which has a significant impact on the frequency and intensity of landslides. Therefore, this study assesses the impact of road expansion on landslide susceptibility in the Himalayas by examining landscape changes within 1 km, 2 km, and 3 km buffer zones around a major highway. Land use and land cover (LULC) from the years 2000 and 2023 were classified using Random Forest (RF) modelling to assess landslide susceptibility due to landscape change. Twelve key parameters were selected for susceptibility modeling and colinearity was tested by multicollinearity analysis to ensure robustness. The RF models were optimised using particle swarm optimisation (PSO) to model landslide susceptibility with higher precision, and their effectiveness was confirmed by receiver operating characteristic (ROC) and precision-recall curves. In addition, a polynomial regression analysis was used to investigate the complex relationships between landscape changes and landslide susceptibility. The susceptibility models showed high accuracy with area under the curve (AUC) values of 0.9083 and 0.9068. The results showed significant landscape changes affecting landslide susceptibility: The forest area decreased by 15.74 km² from 2000 to 2023, while the built-up area increased by 15.41 km². In particular, the zone with very low susceptibility to landslides within the 1 km buffer decreased by 5.569 km² and the zone with high susceptibility within the 3 km buffer increased by 4.972 km². The polynomial regression analysis showed that the built-up areas near the motorway decreased the most by 1.260 km², while the forest areas further away from the corridor increased by up to 4.585 km². This analysis illustrates the significant influence of road proximity on landscape dynamics and landslide risk and provides important insights for policy and land use planning in the rapidly developing Himalayan regions. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to examine the state of infrastructure facilities and its impact on the prosperity of Tricity. Design/methodology/approach: A literature review was carried out, monitoring and analysis of statistical data on the condition of infrastructure facilities in the Tricity agglomeration, as well as some other regions of Poland, was carried out, which also made it possible to compare the situation in Tricity with that places. Findings: The analysis of statistical data shows the problems of traffic congestion and the need to develop the road network in the Tricity agglomeration, as well as the need to develop the cycleway network. An analysis of the characteristics of the water and wastewater infrastructure of the Tricity agglomeration shows its good condition and revealed the presence of a reserve of capacity to unlock the economic potential of the region. Practical/social implications: Identifying the needs for the development of road networks in the region is of practical importance for both local authorities and local society. The presence of a problem should pose a task to find its optimal solutions, which requires both physical capabilities and financial resources. Originality/value: This study emphasizes the importance of analyzing the state of the infrastructure of a specific region, in this case, the Tricity agglomeration. The ratio of the population to the quantitative indicators of certain types of infrastructure facilities, as well as other methods of analyzing available statistical data, made it possible to identify both problems and development prospects. [ABSTRACT FROM AUTHOR]

Objectives: Ridesharing is an essential part of shared mobility for improving passengers' travel efficiency in cities. The existing studies usually determine shareable trips based on whether the arrival sequence of vehicles in more than one pick-up and drop-off points can meet the predefined spatiotemporal constraints. Such a simple approach cannot quickly and comprehensively find all the potential shareable trips under scenarios involving large-scale car-sharing requests. Methods: Based on the modeling method of space-time prism and the spatial-temporal expression of passengers' sharing willing, we first propose a potential spatiotemporal path area model of travel. Then, we apply the topological relation between the spacetime prisms of trips for ridesharing identification, and quantify the strength of ridesharing of trips. Finally, two ridesharing matching strategies are proposed to simulate the ridesharing matching process in real-world transport environment. Results: The proposed ridesharing identification model can accurately delineate the potential space-time accessibility of vehicular travel, which makes it easier to discover all potential shareable trips and to realize the accurate and effective ridesharing identification. Conclusions: This study can be helpful for vehicle dispatching and passengers' travel planning in shared urban mobility system. [ABSTRACT FROM AUTHOR]

Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities. Additionally, it incorporates OpenStreetMap's road network data to study vulnerability, offering a descriptive statistical interpretation. Through spatial overlay techniques, road segments are evaluated for flood risk based on their proximity to identified hazard zones. This method facilitates the detailed mapping of flood-impacted road networks, providing essential insights for infrastructure planning, emergency preparedness, and mitigation strategies. The study emphasizes the importance of integrating geospatial analysis tools with open data to enhance the resilience of critical infrastructure against natural hazards. The resulting maps are instrumental for understanding the impact of floods on transportation infrastructures and aiding informed decision-making for policymakers, the insurance industry, and road infrastructure asset managers. [ABSTRACT FROM AUTHOR]

To mitigate the negative effects of freight vehicles on urban areas, many cities have implemented road accessibility restrictions, including limited traffic zones, which restrict access to specific areas during certain times of the day. Implementing these zones creates a tradeoff between the delivery cost and time, even under the assumption of equal traversal time and travel cost. Consequently, the planners in charge of vehicle routing need to work with graphs containing information on all Pareto-optimal paths. Inspired by these changes in city logistics and the resulting computational challenges, we study the vehicle routing problem with access restrictions, where some streets are closed to traffic within a given time period. We formulate this problem using workday variables and propose two branch and price algorithms based on the underlying road network and multigraph. The results of our computational experiments demonstrate the effectiveness of the proposed algorithms, solving instances with up to 100 nodes and 33 customers, and underline the importance of considering alternative paths in reducing costs. Funding: This work was supported by KU Leuven [C14/22/026]. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Nepal is a country known for its diverse and challenging topography, and it relies heavily on a robust road infrastructure network to connect its remote regions and urban centers. This study addresses the critical need for enhanced road safety and infrastructure resilience on the Siddhababa road section of the Siddhartha Highway, Nepal, notorious for its high accident rates and susceptibility to landslides. Given the road's strategic importance in connecting remote regions and its challenging topographical conditions, our research aimed to identify the most suitable pavement type to mitigate these issues. Through a detailed examination incorporating eight different soil tests, alongside evaluations of traffic loads, weather conditions, and existing pavement performance, we adopted a comparative analysis methodology to assess the viability of flexible versus rigid pavements within this unique context. Results revealed that the soil composition and environmental conditions of the Siddhababa section significantly influence pavement performance, with specific gravity, moisture content, and California Bearing Ratio (CBR) tests indicating a nuanced suitability for both pavement types under varying circumstances. Our analysis concluded that, despite the economic and staged reinforcement benefits of flexible pavements, the durability, safety, and maintenance considerations favor the adoption of rigid pavement for the Siddhababa road section. However, acknowledging the economic constraints, a hybrid approach is recommended, emphasizing rigid pavements for the most vulnerable sections and flexible pavements elsewhere. This study contributes to the pavement engineering field by providing a model for pavement type selection in mountainous regions, aiming to enhance road safety and durability amidst challenging environmental conditions.

The community's resilience in the face of natural hazards relies heavily on the rapid and efficient restoration of electric power networks, which plays a critical role in emergency response, economic recovery, and the functionality of essential lifeline and social infrastructure systems. Leveraging the recent data revolution, the digital twin (DT) concept emerges as a promising tool to enhance the effectiveness of post-disaster recovery efforts. This paper introduces a novel framework for post-hurricane electric power restoration using a hybrid DT approach that combines physics-based and data-driven models by utilizing a dynamic Bayesian network. By capturing the complexities of power system dynamics and incorporating the road network's influence, the framework offers a comprehensive methodology to guide real-time power restoration efforts in post-disaster scenarios. A discrete event simulation is conducted to demonstrate the proposed framework's efficacy. The study showcases how the electric power restoration DT can be monitored and updated in real-time, reflecting changing conditions and facilitating adaptive decision-making. Furthermore, it demonstrates the framework's flexibility to allow decision-makers to prioritize essential, residential, and business facilities and compare different restoration plans and their potential effect on the community.

Abstract In the context of contemporary urbanization, the significance of diversifying urban public facilities has attracted significant attention. This study examines the relationship between road network and the diversity of urban public facilities in Changsha City. These factors are measured through the Shannon-Weiner Index and Space Syntax method. To provide greater specificity, the study employs Random Forest and Geographically Weighted Regression models to analyze the relationship between road networks and the diversity of urban public facilities. The results identify a nonlinear relationship between these variables. In addition, high accessibility exhibits a stronger association with diversity than accessibility alone, and this correlation is reflected in varying degrees of inconsistency across different study areas. Finally, the cartographic depiction of diversity clusters is overlaid on the road network, demonstrating a significant relationship between the configuration of the road network and diversity patterns. In conclusion, this research emphasizes the robust correlation between the roadway network and the diversity of urban public facilities. It prompts local governments to focus beyond mere equality, with a greater commitment to enhancing the quality of life for residents through diversity.

Transportation constitutes one of the pivotal systems crucial to the advancement of so-cioeconomic conditions. However, numerous communities, particularly in Nigeria, face a multitude of transportation challenges. The primary objective of this study is to examine the spatial arrangement of transportation facilities in Adamawa State, Nigeria, with the intention of identifying spatial-related challenges confronting the transportation sector and the socioeconomic development of the state. During the period spanning 2018 to 2019, an investigation into the spatial distribution pattern of transport facilities within the state was conducted. To gather data pertaining to the geographical concentration and developmental trajectory of the transportation system in the state, the ArcGIS 10.5 Length Calculation Module, Google Earth Pro, and questionnaire administration were employed. Data were subjected to inferential statistical analysis, results were tabulated, and maps were generated. The findings unveiled three distinct modes of transit, namely road, water, and air, with roads emerging as the most prominent option. Furthermore, the state exhibited a higher prevalence of paved (engineered but untarred) roads compared to tarred roads. Moreover, an analysis of the spatial pattern encompassing the state’s population and road network revealed that the length and density of tarred roads were greater in less populated Local Government Areas as opposed to the more populated ones. Ultimately, the road network pattern has yielded negative consequences for the state’s development. In light of these findings, the study puts forth recommendations that emphasize the urgent need to tar the numerous paved roads within the state in order to enhance connectivity with markets and other urban-centric facilities.

In the context of contemporary urbanization, the significance of diversifying urban public facilities has attracted significant attention. This study examines the relationship between road network and the diversity of urban public facilities in Changsha City. These factors are measured through the Shannon-Weiner Index and Space Syntax method. To provide greater specificity, the study employs Random Forest and Geographically Weighted Regression models to analyze the relationship between road networks and the diversity of urban public facilities. The results identify a nonlinear relationship between these variables. In addition, high accessibility exhibits a stronger association with diversity than accessibility alone, and this correlation is reflected in varying degrees of inconsistency across different study areas. Finally, the cartographic depiction of diversity clusters is overlaid on the road network, demonstrating a significant relationship between the configuration of the road network and diversity patterns. In conclusion, this research emphasizes the robust correlation between the roadway network and the diversity of urban public facilities. It prompts local governments to focus beyond mere equality, with a greater commitment to enhancing the quality of life for residents through diversity. [ABSTRACT FROM AUTHOR]

This study assessed the impact of improved urban road network on financial performance of Small and Medium Enterprises focusing in Dodoma, Tanzania. The study employed a cross sectional research design where by a sample of 89 SMEs from the selected three wards in Dodoma city was used to draw the conclusion. Qualitative data collected through questionnaires were analyzed using content analysis while quantitative data analyzed using descriptive statistics and multiple regression analysis. The key findings from the study include: i) accessible roads to customers toward SME were having positive influence to SMEs performance ii) market accessibility by customers and consumers was the key factor in improving SMEs financial performance in Dodoma city and lastly, an effective time management and improved transportation of goods and services were positive related to the amount of sales revenue generated by SMEs. The conclusion is drawn to improve the urban road networks as among of the key factors in improving the performance of SMEs in Dodoma city by increasing sales and profit. It is therefore recommended that, the government and other stakeholders in the construction sector should continue to improve urban road networks and start offering basic business and financial management skills which would enable SMEs owners to make informed investment decisions as well as enhance their entrepreneurial skills which will facilitate in recognizing and exploiting the available business opportunities. [ABSTRACT FROM AUTHOR]

The major concerns of e-commerce are the optimization of order dispatch operations and delivery time prediction. Finding the optimal path is considered an important problem in logistics systems. Efficient order distribution is crucial for enhancing logistic operations, especially in urban environments like the city of Tetovo. Therefore, proper prediction and optimization for delivery operations are required for optimal logistics management. This study proposes a novel approach to optimize order distribution utilizing Dijkstra’s Algorithm, a wellestablished method in graph theory and network analysis. To ensure better visibility of the logistics activities and avoid possible obstacles it is suitable to adopt a graph-based method. By modeling Tetovo city’s road network as a graph, with intersections as nodes and roads as edges, the algorithm calculates the shortest paths between various distribution points, such as warehouses and delivery destinations. From the drop points, we will get the information and data about the delivery address, weight of goods packages, and number of customer packages carried by a courier in one delivery transfer by truck delivery logistics. In the meantime, the data about the distance from the distribution points to the delivery destinations will be obtained from the Open Street Maps applications. All the data above will be used to construct a connected weight graph as an initial model graph. Using a methodology based on Dijkstra’s Algorithm, we will determine the shortest or fastest route for delivery service in logistics distribution. By denoting the location of a drop point as a starting point of the route and the road connecting two locations as an edge where the distance traveled from the drop point to the customers or from one customer to another will represent the weight in the graph, we can apply the Dijkstra’s algorithm to optimize the mileage of the delivery packages. The optimization process aims to reduce delivery time and minimize costs while maximizing resource utilization and customer satisfaction. The implementation of Dijkstra’s Algorithm in this context involves factors like traffic congestion, road conditions, and delivery priorities. This algorithm works by finding the lowest cost path with various costs associated with the edges enabling the most efficient way of completing an order within a factory or warehouse. We represent a JavaScript implementation of this algorithm. Through simulation and empirical validation, the proposed approach demonstrates significant improvements in order distribution efficiency compared to traditional methods. This research contributes to the advancement of logistic optimization techniques and provides practical insights for enhancing urban delivery systems in the city of Tetovo and similar metropolitan areas. [ABSTRACT FROM AUTHOR]

Multilane roads in vector data are comprised of parallel line features that represent the same road. The extraction of these features is crucial for updating maps and cartographic generalization. This article addresses previous object-level extraction limitations by introducing a mesh-based method that employs a GCNN model to classify them based on geometric features and proximity relationship. A bidirectional region growing clustering method (BRGC) is designed to cluster the identified multilane road meshes belonging to the same road into groups considering their spatial relationships. The object-level multilane roads are extracted based on the clustering results. Validation using road data from OpenStreetMap in Chengdu and Zhengzhou achieved a classification f1-score >90% for multilane road mesh. Moreover, the object-level multilane detection precision and recall were 88.05% and 92.72% on the Chengdu dataset, and 86.99% and 91.37% on the Zhengzhou dataset, which demonstrates the proposed method effectively extracts multilane roads at the object level, aligning with real-world scenarios and human cognition. [ABSTRACT FROM AUTHOR]

New Hanover County, North Carolina, has been experiencing rapid population growth and is expected to continue this growth, leading to increased land use and development in the area. The county is also threatened by sea level rise (SLR) and its effects because of its coastal location and frequent occurrences of major storms and hurricanes. This study used a land change modeler to map the land cover change throughout the county over a period of 20 years, and predicted land cover distribution in the area in the years 2030 and 2050. Statistics revealed that the developed land in the area increased by 85 km2 between 2000 and 2010, and by 60 km2 between 2010 and 2020. Such land is predicted to increase by another 73 km2 by 2030, and 63 km2 by 2050. This increase in development is expected to occur mainly in the central area of the county and along the barrier islands. Modeling of SLR illustrated that the northwestern part of New Hanover County along the Cape Fear River, as well as the beach towns located on the barrier islands, are estimated be the most affected locations. Results indicate that sections of major highways throughout the county, including I-140 near downtown Wilmington and US-421 in Carolina Beach, may be inundated by SLR, which might delay residents during mandatory evacuations for emergency situations such as hurricanes. Some routes may be unusable, leading to traffic congestion on other routes, which may impede some residents from reaching safety before the emergency. Wrightsville Beach and Carolina Beach are estimated to have the highest levels of inundation, with 71.17% and 40.58% of their land being inundated under the most extreme SLR scenario of 3 m, respectively. The use of the present research approach may provide a practical, quick, and low-cost method in modeling rapidly growing urban areas along the eastern United States coastline and locating areas at potential risk of future SLR inundation. [ABSTRACT FROM AUTHOR]

Disruptions in road networks can significantly impact traffic flow, necessitating the identification and mitigation of vulnerable components. This study presents a methodology to assess the vulnerability and robustness of road networks, addressing the research gap in comprehensive approaches that effectively evaluate both controlled intersections and uncontrolled/free-flow highway segments. The proposed framework employs traffic simulation tools to model road closures within designated study areas, focusing on networks in Qatar. Performance measures such as average control delay and volume to capacity ratios are utilized to calculate vulnerability indices for network components. The two-pronged approach quantifies the impact of simulated closures at both the component and network levels, revealing critical intersections and road segments that markedly affect network performance during disruptions. The findings contribute to the field by providing transportation engineers with an effective quantitative tool to detect vulnerabilities in their road network, guiding design plans and actions to mitigate the potential harmful impacts of disruptive events on transportation infrastructure. The methodology's scalability ensures its applicability to various operational contexts, offering significant insights for transportation planning and infrastructure resilience. By establishing a comprehensive vulnerability analysis framework, this study enhances the understanding and management of road network vulnerabilities in complex urban environments. Further research on integrating dynamic traffic assignment and real-time data is recommended to further enhance the predictive accuracy and applicability of the proposed framework, ultimately improving transportation infrastructure resilience.

Geographic Information Systems (GIS) have become an essential tool in health planning, allowing for the analysis and visualization of spatial data to improve healthcare. In Algeria, the use of GIS in oncological care is still in its early stages, but has the potential to greatly enhance the planning and delivery of cancer services. This study aims to assess the contribution of GIS to health planning in the provision of oncological care in Algeria. Specifically, we will examine how Network analysis can be used to identify areas with high accessibility, map out healthcare facilities and resources, and optimize the allocation of resources to improve access to cancer treatment.A review of existing literature on the use of GIS in health planning and oncological care will be conducted, along with an analysis of cancer incidence data in Algeria. GIS software will be used to map out accessibility rates, healthcare facilities, and population distribution to identify areas in need of improved oncological services. The results of this study will provide valuable insights into how GIS can be utilized to enhance health planning in the provision of oncological care in Algeria. By identifying areas with high accessibility rates and mapping out healthcare resources, policymakers and healthcare providers can better allocate resources to improve access to cancer treatment and ultimately improve patient outcomes.

Abstract Reconstructing ancient transportation networks is critical to studying past human mobility patterns. China’s Haidai region was a thriving political and economic hub during the Bronze and Early Iron Ages. We used GIS spatial analysis techniques to build a “Settlement Interaction Model” based on archaeological data from the Haidai region during the Bronze and Early Iron Age (Shang Dynasty, Western Zhou Dynasty, Spring & Autumn Period, and Warring States period). The eight-level road network maps with traffic attributes were distinguished based on topography and settlement size. The total lengths of the road networks were estimated to be 19,112 km in the Shang Dynasty, 35,269 km in the Western Zhou Dynasty, 51,555 km in the Spring & Autumn Period, and 77,456 km in the Warring States Period, with the average road flows of 6.6, 31.7, 42.8, and 75.5, respectively. The Z score and one-sample t-test (p

It is necessary to evaluate the construction effect of viaducts and identify the viaducts that play a key role in road networks. Based on the node deletion method, this article proposes a method to identify the importance of viaducts in road networks. After applying this method to simulate the importance of viaducts in the highway networks in Yunnan and Guizhou Provinces from 2001 to 2020, the results show the following: (1) The viaducts with high importance were mainly built in 2002, 2009, 2015, and 2016. They are mainly distributed on expressways such as the HUKUN Expressway, HANGRUI Expressway, and YINBAI Expressway. Among the viaducts, the Mengzhai Bridge and Beipanjiang Bridge Hukun are the most important. (2) The importance of viaducts will increase, decrease, or increase first and then decrease. Among the years studied, 2012 and 2016 are important time nodes for change. The trend of changes is affected by the construction of highways and viaducts in other locations. In this road network, there are strong coupling relationships between nodes. (3) The importance of some viaducts is not prominent in the whole region, but that does not mean their construction value is low. They may have a high connectivity effect on specific regions from a local perspective.

Multi-Agent Reinforcement Learning (MARL) has shown strong advantages in urban multi-intersection traffic signal control, but it also suffers from the problems of non-smooth environment and inter-agent coordination. However, most of the existing research on MARL traffic signal control has focused on designing efficient communication to solve the environment non-smoothness problem, while neglecting the coordination between agents. In order to coordinate among agents, this paper combines MARL and the regional mixed-strategy Nash equilibrium to construct a Deep Convolutional Nash Policy Gradient Traffic Signal Control (DCNPG-TSC) model, which enables agents to perceive the traffic environment in a wider range and achieves effective agent communication and collaboration. Additionally, a Multi-Agent Distributional Nash Policy Gradient (MADNPG) algorithm is proposed in this model, which is the first time the mixed-strategy Nash equilibrium is used for the improvement in the Multi-Agent Deep Deterministic Policy Gradient algorithm traffic signal control strategy to provide the optimal signal phase for each intersection. In addition, the eco-mobility concept is integrated into MARL traffic signal control to reduce pollutant emissions at intersections. Finally, simulation results in synthetic and real-world traffic road networks show that DCNPG-TSC outperforms other state-of-the-art MARL traffic signal control methods in almost all performance metrics, because it can aggregate the information of neighboring agents and optimize the agent's decisions through gaming to find an optimal joint equilibrium strategy for the traffic road network. [ABSTRACT FROM AUTHOR]

Floods pose a significant threat to road networks globally, disrupting transportation, isolating communities, and causing economic losses. This study proposes a four-stage methodology (avoidance, endurance, recovery, and adaptability) to enhance the resilience of road networks. We combine analysis of constructed assets and asset system performance with multiple disaster scenarios (Reactive Flood Response, Proactive Resilience Planning, and Early Warning Systems). Advanced flood Geospatial-AI models and open data sources pinpoint high-risk zones affecting crucial routes. The study investigates how resilient assets and infrastructure scenarios improve outcomes within Urban Resilience Index (CRI) planning, integrating performance metrics with cost–benefit analysis to identify effective and economically viable solutions. A case study on the Lisbon Road network subjected to flood risk analyzes the effectiveness and efficiency of these scenarios, through loss and gain cost analysis. Scenario 2, Proactive Resilience Planning, demonstrates a 7.6% increase compared to Scenario 1, Reactive Flood Response, and a 3.5% increase compared to Scenario 3, Early Warning Systems Implementation. By considering asset performance, risk optimization, and cost, the study supports resilient infrastructure strategies that minimize economic impacts, while enabling communities to withstand and recover from flood events. Integrating performance and cost–benefit analysis ensures the sustainability and feasibility of risk reduction measures. [ABSTRACT FROM AUTHOR]

Copyright of Knowledge: International Journal is the property of Institute for Knowledge Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Computing the shortest path between two vertices is a fundamental problem in road networks. Most of the existing works assume that the edges in the road networks have no labels, but in many real applications, the edges have labels and label constraints may be placed on the edges appearing on a valid shortest path. Hence, we study the label-constrained shortest path queries in this paper. In order to process such queries efficiently, we adopt an index-based approach and propose a novel index structure, LSD - Index , based on tree decomposition. With LSD - Index , we design efficient query processing and index construction algorithms with good performance guarantees. Moreover, due to the dynamic properties of real-world networks, we also devise index maintenance algorithms that can maintain the index efficiently. To evaluate the performance of proposed methods, we conduct extensive experimental studies using large real road networks including the whole USA road network. Compared with the state-of-the-art approach, the experimental results demonstrate that our algorithm not only achieves up to two orders of magnitude speedup in query processing time but also consumes much less index space. Meanwhile, the experimental results also show that the index can also be efficiently constructed and maintained for dynamic graphs. [ABSTRACT FROM AUTHOR]

The article discusses the issues of spatial unevenness of transport demand in the city by various transport modes. It describes the creation of models using an artificial neural network to estimate the travel time and share by private and public transport in a large city that does not have off-street transport. The city transport macromodel in PTV Visum (V.18) was used as a data source, from which data were obtained on 50 basic parameters taken into account in the specialized software during the development of the transport model. In total, 50 factors act as independent variables that do not have linear relationships with each other and with the dependent variable, which significantly complicates the use of other models. These models allow assessing the influence degree of the most important factors. Further, the article shows the uneven spatial distribution of the shares of trips by private and public transport across the areas of a city. Using machine learning methods, the transport areas of Tyumen were clustered into nine classes belonging to the central sector, where the share of public transport is significantly higher than at the city border. The dependence of the trip share by cars and shuttle buses on the average travel time and distance by private and public transport for each class of transport areas has been established. The research results can be used when creating new transport areas in the city macromodel and when adjusting transport planning documents. The methods used for analyzing big data on the operation of the transport complex can be implemented in the digital twin of the city and the Intelligent Transport System. [ABSTRACT FROM AUTHOR]

Geographic Information Systems (GIS) have become an essential tool in health planning, allowing for the analysis and visualization of spatial data to improve healthcare. In Algeria, the use of GIS in oncological care is still in its early stages, but has the potential to greatly enhance the planning and delivery of cancer services. This study aims to assess the contribution of GIS to health planning in the provision of oncological care in Algeria. Specifically, we will examine how Network analysis can be used to identify areas with high accessibility, map out healthcare facilities and resources, and optimize the allocation of resources to improve access to cancer treatment.A review of existing literature on the use of GIS in health planning and oncological care will be conducted, along with an analysis of cancer incidence data in Algeria. GIS software will be used to map out accessibility rates, healthcare facilities, and population distribution to identify areas in need of improved oncological services. The results of this study will provide valuable insights into how GIS can be utilized to enhance health planning in the provision of oncological care in Algeria. By identifying areas with high accessibility rates and mapping out healthcare resources, policymakers and healthcare providers can better allocate resources to improve access to cancer treatment and ultimately improve patient outcomes. [ABSTRACT FROM AUTHOR]

Forest roads provide access to remote wooded areas, serving as key transportation routes and contributing to human impact on the local environment. However, large animals, such as bears (Ursus sp.), moose (Alces alces), and caribou (Rangifer tarandus caribou), are affected by their presence. Many publicly available road layers are outdated or inaccurate, making the assessment of landscape objectives difficult. To address these gaps in road location data, we employ CubeSat Imagery from the Planet constellation to predict the occurrence of road probabilities using a SegNet Convolutional Neural Network. Our research examines the potential of a pre-trained neural network (VGG-16 trained on ImageNet) transferred to the remote sensing domain. The classification is refined through post-processing, which considers spatial misalignment and road width variability. On a withheld test subset, we achieve an overall accuracy of 99.1%, a precision of 76.1%, and a recall of 91.2% (F1-Score: 83.0%) after considering these effects. We investigate the performance with respect to canopy coverage using a spectral greenness index, topography (slope and aspect), and land cover metrics. Results found that predictions are best in flat areas, with low to medium canopy coverage, and in the forest (coniferous and deciduous) land cover classes. The results are vectorized into a drivable road network, allowing for vector-based routing and coverage analyses. Our approach digitized 14,359 km of roads in a 23,500 km2 area in British Columbia, Canada. Compared to a governmental dataset, our method missed 10,869 km but detected an additional 5774 km of roads connected to the network. Finally, we use the detected road locations to investigate road age by accessing an archive of Landsat data, allowing spatiotemporal modelling of road access to remote areas. This provides important information on the development of the road network over time and the calculation of impacts, such as cumulative effects on wildlife. [ABSTRACT FROM AUTHOR]

The frequency of extreme weather events has increased worldwide, leading to more intense natural disasters, which pose significant threats to human life and property safety. The main form of disaster occurrence is multi-hazard coupling and multi-hazard chaining. This paper constructs a road natural disaster risk assessment model using a fuzzy comprehensive evaluation method and neural network to quantitatively analyze road disasters with multiple hazards, and provides valuable insights for the predication of road natural disaster risk. Here, ten factors, including temperature, relative humidity, precipitation, elevation, slope, slope orientation, vegetation cover, geologic lithology, historical impact factors, and road density, were selected as input variables, and risk grade was selected as the output value (the evaluation results). The remaining hidden layers use the fully connected neural network. This model was first trained using historical data (from 2011 to 2021) obtained from road networks and natural disasters in Guangxi, China. Then, taking Lingchuan County as an example, the model was used to predict the risk of natural disasters on its roads, and, finally, the prediction accuracy of the model was determined by comparing the results with actual disaster situations. This study can provide theoretical support and technical operations for the development of subsequent early warning systems. [ABSTRACT FROM AUTHOR]

This study took the Poyang Lake ecological economic zone as the research area, and using morphological spatial pattern analysis, explored the morphological spatial characteristics of the fragmentation of cultivated land from 2000 to 2020. Furthermore, based on land use transition matrix methods, spillover effect analysis, and landscape pattern index analysis, this study analyzed the spatiotemporal characteristics in which construction land expands by occupying cultivated land as well as the spatiotemporal response of the landscape pattern of the cultivated land. From 2000 to 2020, the degree of cultivated land fragmentation gradually strengthened and the reduction rate of the core area increased from 1.33% to 6.27%. At the same time, it was shown that the regional difference was gradually strengthened from the plain around Poyang Lake to the mountainous and hilly areas, and the expansion of construction land led to the fragmentation of cultivated land. From 2000 to 2020, the area of cultivated land occupied by construction land increased from 268.60 km² to 1 003.26 km², and mainly occurred in areas with a mature road network. The spillover effect of construction land expansion is gradually increasing, and it is more significant in areas with a mature road network. The expansion of construction land has a strong positive effect on cultivated landscape fragmentation, and road construction strengthens this effect. Meanwhile, cultivated landscape fragmentation shows a greater difference with the reduction of road buffer distance. The degree of cultivated land fragmentation is significantly affected by topography and construction land expansion. In addition, the fragmentation of cultivated land mainly occurs in areas with large relief or dense construction land and a mature road network. [ABSTRACT FROM AUTHOR]

Urban morphology has been empirically demonstrated to be self-organized and can be quantified by fractal dimension. However, the spatial variation rule of fractal features at the sub-zone scale has yet to be uncovered, as well as the relationship between fractal dimension values and road network or land-use patterns. In this study, the urban area is partitioned into 158 grid units, with subsequent calculations conducted to determine the fractal dimensions (using 2D box-counting and 3D voxel-counting methods), road network characteristics, and land-use patterns within each individual unit. The pattern of how architectures fill into the 2D or 3D embedding space at the grid level is revealed. Moreover, the spatial relationship between the road network, land-use, and their impacts on the local architectural layout is elucidated by employing MGWR, a model that incorporates the principles of fitting localized spatial regression. The results are as follows: (1) urban morphology follows fractal laws at a sub-zone scale, both in a 2D plane and 3D volume; (2) the filling degree of architecture is high in the urban center but low in the periphery areas; (3) the selected variables fit well with the regression models; (4) there is spatial heterogeneity regarding the influence of each factor. The research findings provide valuable insights into the theoretical relationship between urban morphology and the composite structure of road networks and land use. This facilitates identifying crucial areas and priority directions for urban renewal construction, as well as optimizing architectural design to improve efficiency and functionality. [ABSTRACT FROM AUTHOR]

Purpose: Recently, the serviceability of the transportation infrastructure in urban areas has become crucial. Any impact of the hazardous conditions on the urban road network causes significant disruption to the functioning of the urban region, making the city's resilience a point of concern. Thereby, the purpose of the study is to examine the city's recovery capacity to absorb the impacts of adverse events like urban floods. Design/methodology/approach: This study examines the road network resilience for an urban flood event for zones proposed by the Municipal Corporation to develop multiple central business districts. This study proposes a novel approach to measure the resilience of road networks in an urban region under floods caused due to heavy rainfall. A novel Road Network Resilience Index (RNRI) based on the serviceability of the road network during floods is proposed, estimated using Analytic Hierarchy Process - Multiple Criteria Evaluation (AHP-MCE) approaches by using the change in street centrality, impervious area and road network density. This study examines and analyses the resilience of road networks in two conditions: flood and nonflood conditions. Resilience was estimated for both the conditions at the city level and the decentralized zone level. Findings: Based on RNRI values, this study identifies zones having a lower or higher resilience index. The central, southern and eastern zones have lower road network resilience and western and northern zones have high road network resilience. Practical implications: The proposed methodology can be used to increase road network resilience within the city under flood conditions. Originality/value: The previous literature on road network resilience concentrates on the physical properties of roads after flood events. This study demonstrates the use of nonstructural measures to improve the resilience of the road network by innovatively using the AHP-MCE approach and street centrality to measure the resilience of the road network. [ABSTRACT FROM AUTHOR]

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Geographic information about the road is an important type of basic geographic information. The extraction of road information plays a crucial role in urban planning, management of traffic, emergency management, and automatic vehicle navigation. With the advancement of remote sensing technology, high-resolution satellite images are becoming more readily available and are improving in quality, making it possible to use these images for accurate road location. The challenge of detecting roads in remote sensing images is complicated by the presence of blurred road edges, sand coverage, and massive terrain objects in complex environments. This paper proposes a deep learning algorithm-based method for extracting road information from remote sensing images to resolve the above problems. The proposed approach consist of two stages, namely pre-processing and segmentation. Initially, satellite images are collected. The collected images may contain noise and blur. This will affect the segmentation output. Thus, we apply an adaptive median filter to the input image before segmentation to eliminate the noise present therein. After pre-processing, the pre-processed image is given to the input of the segmentation process. For segmentation, we introduced a novel optimized multi-path cascade convolution neural network. The proposed multi-path cascade convolution neural network is enhanced using the adaptive artificial jelly optimization (A2JO) algorithm. Finally, from the deep learning model, we obtain the segmented road region. The proposed approach is compared with various state-of-the-art approaches in terms of accuracy, precision, sensitivity, F-score, recall, and specificity. [ABSTRACT FROM AUTHOR]

This study investigates optimal routing with a road network to minimize the total traveling distance among scattered farm fields for smart agriculture. Based on the case of an agricultural corporation in Japan, the shortest paths between all pairs of farm fields and their distances are obtained by utilizing road network data, and the optimal route is searched by addressing the traveling salesman problem (TSP) with an exact algorithm, which is implemented by the COIN-OR branch and cut (CBC) solver. The applicability of the TSP with the exact algorithm is verified by comparing its computational time and solution to those of the TSP with the 2-opt method, an approximation algorithm, through a computational experiment. Moreover, the effectiveness of the proposed approach is demonstrated through a case study on the agricultural corporation considered. The results of the computational experiment and case study show that the proposed approach can provide the optimal route for daily work within 7.5 seconds and can reduce 6.89% of the traveling distance from that of the route a worker creates empirically.

The study carried out the zoning of the city of Shelekhov, based on the assessment of acoustic pollution of the territory. Field observations were made and the number of vehicles moving along intracity roads and the federal highway R-258 “Baikal” was calculated. With the help of sound level meters, instrumental measurements were made in the daytime and at night, and maps of the sound pressure level of the road network were built. The calculation of statistical characteristics was carried out and the daily course of the distribution of the noise level on the main and secondary roads was constructed. The analysis of the obtained results and their comparison with the established sanitary standards for their excess was carried out. Evidence has been identified to support the implementation of strategies aimed at reducing acoustic pollution and, as a result, creating favorable conditions for the population to live.

Forest roads have been recognized as one of the significant contributors to soil erosion processes in forested areas. The construction and maintenance of forest roads can cause severe environmental impacts, including soil erosion, sedimentation, and degradation of aquatic ecosystems. The main objective of the present study is to analyze the impact of forest road networks on soil erosion and sedimentation in the context of the Zagros forestlands, Iran. This study aims to assess the soil erosion and sedimentation on forest roads in four case studies in the Zagros forestlands. This study collected data using field surveys and SEDMODL equations to determine input factors and sedimentation and erosion rates. This study found that roadside erosion is strongly correlated with geological factors, road width, and precipitation factors. The height changes of 144 benchmarks were recorded during one study year (2021–2022) on four study roads, and the measured results of erosion benchmarks indicated an average soil erosion of 3, 2.6, 4.7, and 3.5 mm per year around the Bideleh, Kohian, Nazi, and Tabarak roads, respectively. This study measured soil erosion and sedimentation at three distances (5, 15, and 25 m) from the road, and found a significant difference in the height changes of the benchmarks at varying distances from the study roads. A hot-spot analysis was conducted using GIS 10.8, and the results indicated that a significant portion of the studied forest roads had very high erosion production and hot spots. The results of the hot-spot analysis indicated that 30.8%, 22.6%, 39.8%, and 14.5% of the study forest roads, Nazi, Tabarak, Bideleh, and Kohian roads, respectively, are identified as areas with very high erosion production and hot spots. These results highlight the need for effective management strategies to minimize the impact of erosion on road infrastructure and the surrounding environment. Overall, this study provides important insights into the soil erosion and sedimentation on forest roads, and the findings presented here can be used to inform future road construction and maintenance.