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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]

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 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.

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

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]

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.)

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]

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]

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]

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.)

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]

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.

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.

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.

Due to their unstructured characteristics, mature convolutional neural network (CNN) models often have difficulty performing spatial analysis with vector data. Current studies used graph neural network (GCN) models to address this problem. However, the definition of cognition factors involves uncertainties, making it challenging to accurately and comprehensively define these factors. In this paper, the road interchange detection task is taken as an example to introduce the MeshCNN, a deep learning model based on triangular mesh data, aiming to provide a new solution for spatial analysis of vector data. A triangular edge classification model is first trained with simple input features. Then, interchanges are detected based on the classification results with an adaptive method. Experiments were conducted on real-world road network data from four cities. The results reveal that the proposed method outperformed the existing methods with precision and recall rate of 89.36% and 79.25% for interchange detection on the total datasets. Furthermore, our proposed method can also detect interchanges in other regions more easily than the GCN method.

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]

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.)

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]

\nK EY P OLICY H IGHLIGHTSThe connectivity of roads under the influence of traffic is determined by the topological connections established by the road network and the transport connections formed by moving vehicles between two roads simultaneously. A thorough examination of this connectivity is crucial for comprehending traffic patterns and providing guidance to address transportation issues such as traffic congestion. However, existing methods ignore the intuitive representation and explanation of this connectivity, limiting the understanding of actual traffic patterns. This study aimed to intuitively visualize the connectivity of roads under the influence of traffic using CiteSpace, a bibliometric tool for analyzing the relationship between literature keywords. Here, the literature was replaced by trajectory, and keywords were replaced by roads. We then presented the connectivity of roads under the influence of traffic in a network, where nodes represented roads and edges represented the topological and transport connections between roads. A case study was conducted in Beijing, demonstrating the dynamic connectivity of roads under the influence of traffic in the visualized network. By comparing the visualized network with the topological road network, we identified three types of road connectivity under the influence of traffic: roads with topological connections but lacking transport connections, roads with transport connections but lacking topological connections, and roads with both topological and transport connections. This finding further revealed the traffic relationship between roads. Additionally, we simultaneously presented the multidimensional traffic characteristics in the visualized network, aiding in predicting traffic states. Road connectivity was visualized considering topological and transport connections simultaneously.Three types of road connectivity under traffic influence were revealed using CiteSpace.Association rules predicted relationships of traffic characteristics to congestion.Road connectivity was visualized considering topological and transport connections simultaneously.Three types of road connectivity under traffic influence were revealed using CiteSpace.Association rules predicted relationships of traffic characteristics to congestion. [ABSTRACT FROM AUTHOR]

Location-based skyline queries in road networks can quickly return the desired data to the user from a large amount of data according to the user's needs and location. However, there is no effective mechanism for calculating the road network distance and judging the dominant relationship between data points in the existing skyline queries algorithms in road networks. In this paper, we propose improved dynamic skyline queries in road networks, which can quickly compute all skyline points in road networks. Firstly, this paper proposes a GD-tree index, which efficiently stores the location information of the target tuple in the road network and speeds up the calculation of the road network distance. In addition, the concept of the optimal scan end point is proposed to reduce the calculation cost. The histogram of frequency statistical is proposed for the non-spatial dimension, and the nearest and farthest distance is calculated for the spatial dimension to select the optimal scan end point. Finally, extensive experiments in real road network datasets demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article examines the impact of transport policies on road accessibility in Ghana. To measure accessibility gains, the article applies a model that compares the area and population that can be reached in under four hours by road from any city in 2010 and 2019. By quantifying travel times required to reach the nearest city ten years apart, we measure whether road improvement projects have led to improving accessibility and in which regions of Ghana. The model suggests that regional disparities tend to grow as Ghana becomes increasingly urbanized. While major accessibility gains have been observed in the last decade, southern regions and cities have benefited more from the improvement and development of the road infrastructure than the rest of the country. Accessibility gains are particularly important in the southwestern part of Ghana, where mining, agriculture, and urbanization are fueled by new feeder roads in previously forested areas. By comparison, accessibility remains poor in the north of the country. The persistence of regional disparities is reinforced by the general distribution of the population in Ghana and the spatial structure of the urban network. [ABSTRACT FROM AUTHOR]

The study aims to develop and assess an algorithm for efficiently generating parking spot layouts within predefined area outlines. The algorithm is an attempt to streamline the decision-making process by producing different design variants and optimizing the utilization of available space. The algorithm's primary objective is to streamline decision-making by generating diverse design variants while optimizing the use of available space, with a distinct focus on mitigating environmental impact and fostering ecological well-being. Researchers conduct thorough tests on the algorithm across various outlines, resulting in multiple layout options for each scenario. They analyzed five representative parking locations and compare the algorithm's results with the existing parking spot layouts. Throughout the evaluation process, they consider quantitative and qualitative data, considering the complexities of communication solutions within each context. The study findings indicate that the algorithm demonstrates comparable or superior performance to existing solutions. Overall, the study highlights the promising potential of algorithmic design approaches in the context of parking lot automated design. Achieving a balance between innovative designs and user-friendly layouts is crucial, and this is achievable by conducting comprehensive analyses that consider various factors. The consistent findings underscore the algorithm's potential to significantly contribute to sustainable design practices in parking lot layouts, highlighting decreased environmental strain, efficient land use, and creating urban spaces that prioritize ecological benefits. Furthermore, seamlessly integrating algorithmic solutions with existing communication systems is paramount to ensure practical applicability in real-world scenarios. This integration will enable more effective and practical implementation of the algorithm's outputs in actual parking lot design projects. [ABSTRACT FROM AUTHOR]

Making decisions about which objects to keep or omit is challenging in map design. This process, called selection, constitutes the first operation in cartographic generalization. In this research, a method of automatic road selection for creating small-scale maps using machine learning and data enrichment is proposed. First, the problem of contextual information scarcity concerning roads in the source database is addressed. Additional information concerning the relations between roads and other objects was added (such as centrality and proximity measures). Second, machine learning is used to design automatic selection models based on enriched information. Third, three different road selection approaches are implemented. The baseline approach is following the official map design guidelines. The second approach is based on machine learning using the enriched road database. The third approach is based on an existing structural model. The results of all approaches are compared to existing atlas maps designed by experienced cartographers. The results of the Machine Learning Approaches were most similar to the atlas maps (between 81% and 90% accuracy). The least efficient approaches were the Structural Approach with 32% and the Guidelines Approach with 44% accuracy. We conclude that enriching road data with new contextual information concerning roads and using machine learning is beneficial as the achieved results outperform both Guidelines and Structural Approaches. [ABSTRACT FROM AUTHOR]

As an important part of maps, road networks have always been the focus of research in map generalization. However, the fully automated quality evaluation of road network generalization has not been implemented. This paper proposes a framework of Sample-Map database to evaluate road network generalization quality using spatial similarity measures. The similarity calculation model considers spatial distribution, topological relations, and distance relations of road networks. A Sample-Maps database is created as an evaluation set for different road network types. The method is tested on multi-scale maps from Shenzhen, China, and validated by psychological cognition experiments and professional cartographers' judgment. [ABSTRACT FROM AUTHOR]

The road network is a large-scale space transportation channel and a small-scale space social connection bridge, and its spatio-temporal complexity emerging mechanism has been a multi-disciplinary research hotspot. Fractal is one of the important tools to explore geographical complexity. This paper collects the road networks data of the top 200 districts in India's total population, excavates the geometric and structural fractal characteristics of the road networks in each district, and analyzes the statistical correlation and spatial similarities and differences of the geometric and structural fractal characteristics. The results show that:①The spatial distribution of the road network in the sample districts in India is uneven, maintaining the pattern of dense in the south and sparse in the north, with coastal density higher than inland. The difference between the north and the south is stronger than that between coastal and inland areas, which is consistent with the urban development pattern and the driving force of urban development in India;②The road network in all districts of India has significant geometric and structural fractal characteristics. The geometric fractal dimensions of the road networks box-covering [1. 229,1. 857], the structural fractal dimensions [1. 623,3. 179], and the fractal dimensions of the volume and degree volume structures [1. 941,3. 539] and [2. 410,3. 822], respectively, indicate that the road networks construction in most districts of India is dominated by spatial spread, thickening and densification, and three-dimensional development level needs to be strengthened, especially the cross grade and multi-layer roads are limited; ③The regional linkage effect of high-grade traffic corridors is obvious, which is conducive to the coordinated development of space. [ABSTRACT FROM AUTHOR]

With the spread of smartphones and mobile internet, Global Positioning System (GPS) data from vehicles has become widely available. This data represents a unique opportunity to automatically extract road network features and generate detailed maps that can be used in the creation of transport network models, while minimising the quantity of resources usually invested in that task. Accurate transport network models can be used in a variety of applications either in transport simulation models or autonomous vehicles navigation. Although two relevant literature reviews were performed during the last decade, they were not systematic and did not explore the road network inference methods from a transport network modelling point of view. The objective of this research is to perform a systematic and reproducible literature review on the use GPS data in transport network modelling and provide limitations and future work to extract a road network representation for transport models and autonomous vehicles navigation. This was done by systematically examining the studies' different approaches with respect to relevant criteria. Most studies produced a simple representation of the road network, not detailed enough for transport models. Other limitations were the bias introduced by the GPS sample and the reproducibility of the different methods. [ABSTRACT FROM AUTHOR]