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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]

Copyright of Anales de Geografía de la Universidad Complutense is the property of Universidad Complutense de Madrid 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 unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban hierarchical spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management. [ABSTRACT FROM AUTHOR]

Road network maintenance scheduling mainly considers budget limits in the previous studies and largely ignores the capital constraints. This study proposes a bi-objective mixed integer programming model, in which the net present value (NPV) is maximized and the increased total system travel time (ITSTT) due to maintenance activities under the capital constraints is minimized. Since the road network flows cannot reach an equilibrium state overnight due to the variation of network capacity, a link-based day-to-day dynamics model is developed to simulate the transient fluctuation in the traffic flows and calculate the total system travel time of each day. The bi-objective model is solved by the nondominated sorting genetic algorithm-II (NSGA-II) that generates a set of optimal Pareto solutions. The TOPSIS method is then adopted to determine the best compromise solution. Finally, a case study is conducted to demonstrate the effects of key parameters on the values of the two objectives. [ABSTRACT FROM AUTHOR]

The present study attempts to analyse the connectivity pattern and development of road network in the East district through connectivity indices such as alpha, beta, gamma the sum of which gives the Aggregate Transport Score (ATS) and the density of roads has also been calculated. The development of Pradhan Mantri Gram Sadak Yojana (PMGSY) roads has been analysed based on the secondary data. It has been found that during 2006 East district had an overall road density of 0.82 of which two of the blocks had the lowest density with only 0.66 and another two of the Gram Panchayat Units (GPUs) had villages having no road network. However, in 2020 the connectivity has improved considerably with an increase in road density of 1.59 and the road density of all the eight blocks is more than 1 indicating a relatively developed network. The road connectivity of GPUs has also improved and only two of the GPU are having low category of ATS and 43 under high ATS. The result of Shannon Entropy shows that Alpha and Beta are more significant than Beta and ATS. A total of 185 habitations have been connected with 646.9 km of newly constructed roads under PMGSY during 2001–2018. [ABSTRACT FROM AUTHOR]

Computing the shortest distance between two vertices is a fundamental problem in road networks. Since a direct search using the Dijkstra's algorithm results in a large search space, researchers resort to indexing-based approaches. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hop-based solutions. However, the hierarchy-based solutions require large search space for long-distance queries, while the hop-based solutions result in high computational waste for short-distance queries. To overcome the drawbacks of both solutions, in this paper, we propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an efficient query processing algorithm with performance guarantees by visiting part of the labels for the source and the destination based on the hierarchy. We propose a novel algorithm to construct the H2H-Indexbased on distance preserved graphs. We also extend the H2H-Indexand propose a set of algorithms to identify the shortest path between vertices. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can achieve a speedup of an order of magnitude in query processing compared to the state-of-the-art while consuming comparable indexing time and index size. [ABSTRACT FROM AUTHOR]

Satellite image analysis has numerous useful applications in various domains. Extracting their visual information has been made easier using remote sensing and deep learning technologies that intelligently interpret clear visual cues. However, satellite information has the potential for more complex tasks, such as recommending business locations and categories based on the implicit patterns and structures of the regions of interest. Nonetheless, this task is significantly more challenging due to the absence of obvious visual cues and the highly similar appearance of each location. This study aims to analyze satellite image similarity between business class categories and investigate the capabilities of state-of-the-art deep learning models for learning non-obvious visual cues. Specifically, a satellite image dataset is constructed using business locations and annotated with the business categories for image structural similarity analysis, followed by business category classification via fine-tuning of deep learning classifiers. The models are then analyzed by visualizing the features learned to determine if they could capture hidden information for such a task. Experiments show that business locations have significantly high SSIM regardless of categories, and deep learning models only recorded a top accuracy of 60%. However, feature visualization using Grad-CAM shows that the models learn biased features and disregard highly informative details such as roads. It is concluded that typical learning models and strategies are insufficient to effectively solve this complex visual problem; thus, further research should be done to formulate solutions for such non-obvious classifications with the potential to support business recommendation applications. [ABSTRACT FROM AUTHOR]

The nearest neighbors query problem on road networks constitutes a crucial aspect of location-oriented services and has useful practical implications; e.g., it can locate the k-nearest hotels. However, researches who study road networks still encounter obstacles due to the method's inherent limitations with respect to object mobility. More popular methods employ indexes to store intermediate results to improve querying time efficiency, but these other methods are often accompanied by high time costs. To balance the costs of time and space, a lightweight flow graph index is proposed to reduce the quantity of candidate nodes, and with this index the results of a kNN query can be efficiently obtained. Experiments on real road networks confirm the efficiency and accuracy of our optimized algorithm. [ABSTRACT FROM AUTHOR]

The evalution of link resilience importance is essential for improving the seismic resilience level of the road network. Using resilience achievement worth (RAW) as evaluation index, an evaluation method of link importance based on seismic resilience of road networks was proposed. With the help of the bidirectional inference ability of dynamic Bayesian network (DBN), taking the initial DBN as the benchmark and the link connectivity at different times as the evidence, the resilience curve of the road network was updated, the RAW was calculated, and the link resilience importance at different times was evaluated. Taking the local road network in Shinan District of Qingdao as an example, the evaluation method of link importance was verified. The results show that the seismic resilience importance varies with each link at the same time. The resilience importance of the same link is positively correlated with maintenance rate. The resilience importance of different links varies in sensitivity to time. The proposed importance evaluation method redefines and quantifies the seismic resilience importance of links at different times, and identifies the links with high resilience importance and sensitivity to time. The post-earthquake recovery strategy that inclines the limited maintenance resources to these links and the pre-earthquake prevention strategy that reinforces the links with higher resilience importance can more efficiently improve the seismic resilience of the road network.

ABSTRACTThe unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban hierarchical spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management.

Visual fragility is a fundamental analysis of landscape quality, especially for protected natural areas highly valued for their biodiversity, geodiversity and cultural heritage. This is the case of the Sierra de las Nieves Protected Natural Area, located in southern Spain, declared a national park in 2021 and which brings together outstanding natural systems. Its aesthetic character is a key point for both the local population and visitors, so phenomena such as fire pose a territorial threat. This study analyses which are the most fragile landscapes from a visual point of view. Thus, using spatial analysis techniques in GIS, the landscape units were delimited and their visual fragility was evaluated considering a 5x5m DEM of spatial resolution and defining visual basins from geolocated points along the road network, which included paved roads, unpaved roads and official trails of the study area. The results indicate a broad spatial variability in visual fragility depending on the observation point, throughout the natural area, although the landscapes with the highly valued forests of Abies pinsapo Boiss. and Quercus alpestris showed the greatest fragility, especially those located in the central, southern and eastern sectors over 900 m above sea level. These results contribute to managing the areas most exposed to threats with a visual impact, such as fire, which can negatively influence the aesthetic character of the landscapes of the Sierra de las Nieves.

This paper summarizes current trends in the research and development of e-Mobility and energy coupled simulation to deal with electric vehicle integration into power systems, optimal charging infrastructure design, and regional energy and environmental impact assessments. Small trials and simulations were introduced as a case study. The car probe and floating population data are input into various electric vehicle dynamic models, in which variable vehicle speed and state-of-charge are precisely considered. Then, the synergic impacts on the mobility and energy sides are evaluated through co-simulation of the road traffic and distributed power system models.

Infrastructural projects at regional scale are ones of the important components of modern digital economy. In relation to the forestry, the main objects that determine infrastructural load of the territory in the forestry are the networks of fire stations and forest roads with settlements and public roads. A method to define a cell size of a regular uniform rectangular grid based on the pixel size of MODIS-based vegetation raster is described. It is based on the statistics of number of settlements, fire stations and road sections, as well as road sections lengths. Exploratory regression was used to choose a cell size with a number of forest fires as a dependent variable and number of settlements, road segments and fires stations as independent variables. It was found that the most optimal size of regular grid cell for Krasnoyark Region (Kray) is 24 km2. Paper’s findings could be used in climate and economic projects to estimate infrastructural load.

The features of the functioning of the on-board information systems of a car are considered. Threats to their security are analyzed, and methods for ensuring information security and functional security of on-board information systems are proposed. The design of road networks in the organization of road traffic is one of the factors in ensuring the functional security of modern intelligent transport systems, that is, compliance with such information security attributes as data confidentiality, integrity, availability, authenticity and novelty of data. The security of on-board vehicle information systems is a critical issue in the modern world, as more and more vehicles are equipped with electronic systems that may be vulnerable to cyber attacks. One of the main challenges of protecting on-board information systems is the wide range of devices and technologies used in modern vehicles. Different systems may have different security requirements and vulnerabilities. They may interact with each other in a complex way. Another challenge is that many of these systems were not designed originally with security in mind. They may lack basic security features such as encryption and authentication and use outdated software and protocols that are vulnerable to known attacks. The main types of attacks and threats to the elements of the transportation system that interact with the VANET were identified to analyze the information security of vehicle in-vehicle systems. Based on the theory of fuzzy sets under conditions of uncertainty and using the Fuzzy Logic Toolbox in the integrated Matlab environment, the level of information security of the OBU-VANET system was modeled. The obtained results allowed us to formulate the degree of information security of vehicle operation elements against unauthorized access to data. The results of the study showed that technical communication systems have the highest security level (> 0.7), and vehicles become the most vulnerable in public places.

A shortest path query on road networks is a fundamental problem, which has been studied widely. Existing studies usually model road networks as a static graph and query the path with the shortest distance between given vertices. However, a road network has time-dependent property, so modeling the road network as a time-dependent graph is more realistic. Compared with the static graph, the time-dependent graph is larger and more complex, which increases the difficulty of time-dependent shortest path query. The time-dependent shortest path refers to the path with the shortest travel time between a source and a destination in a time-dependent graph under a given departure time. Therefore, the result of the time-dependent shortest path is impacted by the given departure time, which brings a new challenge for the query. These difficulties and challenges make the traditional shortest path algorithms not applicable to the time-dependent shortest path query. This paper employs a time-dependent graph to model the road network, and TD-H2H (time dependent-hierarchical 2-hop) index is proposed based on the tree decomposition, which can be used to quickly and accurately solve the time-dependent shortest path problem. Firstly, a time-dependent tree decomposition algorithm based on the traditional tree decomposition is presented to transform the time-dependent graph into a tree structure. Then, the index structure is quickly determined by tree decomposition, and an efficient index construction algorithm is proposed, denoted as TD-H2H. Finally, based on the TD-H2H index, an efficient time-dependent shortest path query algorithm is designed, named as TD-OAI. Experiments with existing algorithms are performed on 4 real-world datasets. Experimental results show that the query speed of the proposed algorithm is 1 to 2 orders of magnitude better than existing algorithms, and prove the effectiveness and efficiency of the proposed algorithms.

AbstractSafety on road networks is the utmost important factor to consider for public well-being and transportation efficiency. This study introduces a new approach that combines Getis-Ord spatial statistics and crash rate analysis to identify significant road traffic accidents (RTAs) characterized by hotspots on road segments in Addis Ababa’s road network. The study’s results visually portray the crash locations, associating them with the underlying road network, which demonstrates a notable concentration of accident hotspots, between the years from 2014 to 2019 on Addis Ababa’s roads network. The RTAs spatial analysis resulted in the identification of hotspots on 33 road segments, 3 intersections, and 10 roundabouts/squares. Among the identified hotspots, the road segment recognized as Djibouti Street, extending from Bole Edna Mall to the 22 “mazoriya” roundabouts, stands out as the most significant accident hotspot. It exhibits an average of 37.5 crashes per kilometer per year, encompassing a road segment length of 1141 m. Using both methods in this study is crucial for validating findings by identifying high-crash segments and enhancing their reliability and hotspot accuracy. This unique validation method links each traffic accident’s spatial data with the road network using both crash rate and spatial statistical analysis, effectively pinpointing accident hotspots. Given the limited resources, this approach enhances awareness of accident-prone locations, enabling the prioritization of safety measures to improve road safety. It effectively addresses spatial analysis gaps related to RTAs in Ethiopia and holds practical significance by identifying and prioritizing safety measures aimed at reducing accidents within Addis Ababa’s road network.

Growing population, rapid urbanization, and increasing travel demand emphasize the need for reliable public transportation systems and sustainable transportation planning. A reliable bus service fosters a more significant, satisfied, and committed base of users. This research focuses on examining the association between on-time performance (OTP) and road network, demographic, socioeconomic, and land use characteristics to identify relevant external factors for proactive and reliable public transportation system planning. The analysis was conducted at a bus stop level. Bus arrival/departure data from the Charlotte Area Transit System (CATS) was obtained. The road network, demographic, socioeconomic, and land use characteristics were captured within 0.25-mile and 0.50-mile buffers. Pearson correlation analysis was conducted to understand the association between OTP and road network, demographic, socioeconomic, and land use characteristics by day of the week and time of the day. The results show that OTP is associated with external factors such as the number of signalized or cul-de-sac/dead-end intersections, number of lanes, network density, population, income (median and total), and land use types related to residential and commercial/employment purposes within the bus stop vicinity. The findings provide vital insights for transit agencies to enhance scheduling, service and maximize benefits, effectively utilize available resources, plan, and provide equitable services to all riders.

ABSTRACTThis paper presents a naïve map that attempts to reflect the vision that Philip II and his advisors probably had of the Spanish road network in the second half of the sixteenth century, a crucial aspect for the choice of the seat of the capital of the kingdom. The elaboration of the naïve map was carried out in two phases: in the first, the road network was reconstructed based on a thorough revision of the primary sources that have survived to the present day. As these sources showed evident problems of completeness, the network was completed using mathematical methods, which were statistically contrasted. The analysis carried out is an important novelty since it shows that most of the transport in the Iberian Peninsula was channeled through the center following a radial structure with six principal axes two centuries before what has been traditionally considered.

The steady increase in data generation by GPS systems poses storage challenges. Previous studies show the need to address trajectory compression. The demand for accuracy and the magnitude of data require effective compression strategies to reduce storage. It is posited that the combination of TD-TR simplification, Kalman noise reduction, and analysis of road network information will improve the compression ratio and margin of error. The GR algorithm is developed, integrating noise reduction and path compression techniques. Experiments are applied with trajectory data sets collected in the cities of California and Beijing. The GR algorithm outperforms similar algorithms in compression ratio and margin of error, improving storage efficiency by up to 89.090%. The combination of proposed techniques presents an efficient solution for GPS trajectory compression, allowing to improve storage in trajectory analysis applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University 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.)

Traffic prediction is an important part of smart city projects. Due to the complex topology of urban road network and the dynamic change of traffic data, establishing a spatio-temporal model to accurately predict traffic volume remains a challenging task at present. Recently, Graph Convolution Networks (GCN) have been widely used to extract features from non-grid data, and time sequence models have been used to learn temporal features of traffic distributions. However, current GCN based methods only make use of the natural structure of road network, while ignoring the information of administrative units, neighborhood units and other hierarchical structures of spatial interaction. Therefore, traditional models are typically developed under one single scale, which are far from the prediction of multi-scale systems. To address this issue, we propose a novel framework, named MmgFra, to merge multi-scale information for high-precision urban traffic flow prediction. MmgFra consists of three components: a spatial feature extraction module, a feature clustering fusion module, and a temporal feature extraction module. Specifically, we use stacking GCNs to extract spatial structure information of the city road network, administrative unit road network, and neighborhood units, and employ a DIFFPOOL module to cluster and fuse the above information. Finally, we introduce GRU to capture temporal features. We evaluated its performance on a real city dataset using different time scales. The experimental results indicate that compared with state-of-the-art neural network models and GCN-based variant models, our model exhibits higher predictive accuracy. MmgFra improves by approximately 8.4%-29.5% and 7.5%-30.6% in terms of RMSE and MAPE metrics, respectively. [ABSTRACT FROM AUTHOR]

Recently, complicated spatial search algorithms have emerged as spatial-information-based applications, such as location-based services (LBS), and have become very diverse and frequent. The aggregate nearest neighbor (ANN) search is an extension of the existing nearest neighbor (NN) search; it finds the object p * that minimizes G { d (p * , q i) , q i ∈ Q } from a set Q of M (≥1) query objects, where G is an aggregate function and d () is the distance between two objects. The flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search by introducing flexibility factor  ϕ (0 < ϕ ≤ 1) ; it finds the object p * that minimizes G { d (p * , q i) , q i ∈ Q ϕ } from Q ϕ , a subset of Q with | Q ϕ | = ϕ | Q | . This paper proposes an efficient ϵ -approximate k-FANN (k ≥ 1) search algorithm for an arbitrary approximation ratio ϵ (≥1) in road networks. In general, ϵ -approximate algorithms are expected to give an improved search performance at the cost of allowing an error ratio of up to the given ϵ. Since the optimal value of ϵ varies greatly depending on applications and cases, the approximate algorithm for an arbitrary ϵ is essential. We prove that the error ratios of the approximate FANN objects returned by our algorithm do not exceed the given ϵ. To the best of our knowledge, our algorithm is the first ϵ -approximate k-FANN search algorithm in road networks for an arbitrary ϵ. Through a series of experiments using various real-world road network datasets, we demonstrated that our approximate algorithm always outperformed the previous state-of-the-art exact algorithm and that the error ratios of the approximate FANN objects were significantly lower than the given ϵ value. [ABSTRACT FROM AUTHOR]

Fractal geometry has provided a new perspective for urban road network morphology research. This study systematically verifies and analyzes the spatial heterogeneity of fractal characteristics and influencing factors of urban road networks using spatial analysis. Here, Tokyo Metropolis was selected as a case, and the fractal dimensions of road networks were calculated. To determine the spatial heterogeneity in the relationship between fractal dimensions and influencing factors, we examined the spatial distribution characteristics of fractal dimensions using spatial autocorrelation analysis, selected population, build-up area density, and road network density as the explanatory variables, and established the global regression model and local regression model using ordinary least squares (OLS) and geographically weighted regression (GWR), respectively. The results indicated that the spatial distribution of fractal dimensions of the urban road network exhibited an obvious tendency toward geographical dependency. Considering the spatial heterogeneity in the relationship between the fractal characteristics of the road network and the influencing factors not only improves the reliability of analysis but also helps planners and decision-makers grasp the morphological characteristics of the urban road network and estimate the evolution of the road network, thereby promoting the development of urban road networks in a more orderly, efficient, and sustainable direction. [ABSTRACT FROM AUTHOR]

Privacy protection and task allocation are two core issues in spatial crowdsourcing. Based on Euclidean space, most of existing researches use geo-indistinguishability (GeoI) to protect location privacy, while ignoring the underlying road network information, which leads to privacy leakage and utility loss of crowdsourcing workers. In order to protect the location privacy of workers and produce small utility loss, a privacy-preserving batch task allocation algorithm framework for road network is proposed. Firstly, the graph-exponential mechanism optimization problem is proposed, and a greedy algorithm is designed to find the approximate optimal solution. At the same time, the edge server is introduced as the privacy protection agent of workers. Then, the task allocation problem is transformed into a bipartite graph maximum flow problem with the weight of workers􀆳 travel distance, and the optimal solution is obtained by using Kuhn Munkras (KM) algorithm. Finally, the experiment results show that the proposed algorithms significantly improves both privacy protection and utility. [ABSTRACT FROM AUTHOR]

The province of Coclé is in the central zone of the Republic of Panama but lacks development of the road network. In order to assess the spatial analysis of territorial accessibility and connectivity, the methodology of this study was divided into three stages: determination of the working scenario, data acquisition, and spatial analyses. The data acquisition includes the GIS development and the field work for GIS validation. Within the province of Coclé, 19.55% of the population lives more than 500 m away from the nearest paved road, with the distance of the town farthest away from the nearest paved road being 36 km, which leads to different levels of accessibility and connectivity in this province. This study of the spatial analysis of connectivity and territorial accessibility sheds light on how the expansion of the road network affects access to health services and education. [ABSTRACT FROM AUTHOR]

Floods are repetitive and unpredictable phenomena. Interventions in these cases are carried out during or after a hazard's occurrence. The quality and density of the roads, the equipment of emergency intervention centers, and the proximity of these centers to risk areas influence accessibility and intervention capacity. An analysis of this issue is carried out in the present article for a small catchment in Romania in a predominantly hilly area. Areas with very high and very low road accessibility are identified. Proximity to emergency centers can reduce travel time, thus increasing the surfaces of well-served areas. [ABSTRACT FROM AUTHOR]

Paper presents an overview of history and current research state on the use of remote sensing data from space to recognize roads for the regional projects in the forestry. We reviewed the principles of road detection on the optical satellite imagery. Group of direct recognition features used in combinations such as brightness and texture, geometry and brightness. Three research directions with examples were identified: visual roads recognition, use of special software and libraries for developers, and neural networks. For the road network detection we have described methods and software, type and spatial resolution of imagery. Road image recognition based on the optical survey from the open and commercial sources, machine learning methods and neural networks. Up-to-date tasks of road recognition are the following: evaluation of road surface condition, modeling of existing roads location, designing and building new roads, roads seasonality. A functional summary of MapFlow plugin for road recognition in Open Source QGIS is given. Paper is a part of regional forestry transport modeling project to access the forest fires and forest resources by ground means.