Search

Abstract Reasonable districting plays an important role in the patrolling process. In this paper, workload attributes are considered, and a mixed integer programming model is developed to solve the street patrol districting problem (SPDP). The improved spectral clustering algorithm named spectral clustering algorithm based on the road network (SCRn) and simulated annealing algorithm (SA) are combined. This results in a hybrid algorithm called SCRn-SA. The SCRn-SA algorithm is tested on small examples and real instances in Zhengzhou, China. The experimental results show that the proposed algorithm is effective for solving SPDP. It has better performance when compared to other advanced algorithms.

Accurate and up-to-date road maps are crucial for numerous applications such as urban planning, automatic vehicle navigation systems, and traffic monitoring systems. However, even in the high resolutions remote sensing images, the background and roads look similar due to the occlusion of trees and buildings, and it is difficult to accurately segment the road network from complex background images. In this research paper, an algorithm based on deep learning was proposed to segment road networks from remote sensing images. This semantic segmentation algorithm was developed with a modified UNet. Because of the lower availability of remote sensing images for semantic segmentation, the data augmentation method was used. Initially, the semantic segmentation network was trained by a large number of training samples using traditional UNet architecture. After then, the number of training samples is reduced gradually, and measures the performance of a traditional UNet model. This basic UNet model gives better results in the form of accuracy, IOU, DICE score, and visualization of the image for the 362 training samples. The idea here is to simply extract road data from remote sensing images. As a result, unlike traditional UNet, there is no need for a deeper neural network encoder-decoder structure. Hence, the number of convolutional layers in the modified UNet is lower than that in the standard UNet. Therefore, the complexity of the deep learning architecture and the training time required by the road network model was reduced. The model performance measured by the intersection over union (IOU) was 93.71% and the average segmentation time of a single image was 0.28 sec. The results showed that the modified UNet could efficiently segment road networks from remote sensing images with identical backgrounds. It can be used under various situations.

Flash flood in urban area strikes mainly the road network. In fact, the streets during flood act as streams or overland flow paths. This jam the traffic, stop the public services and interrupt the economic activities. The previous studies used to deal with urban flood as it happens in a rural area. This study presents a new approach that deal with road network as stream flow path. The new approach utilizes 3D city model to generate a modified Digital Elevation Model (DEM) which will be the base for hydrology analysis. This procedure considers the building and streets as part of topography which results the water flows through streets as reality. The flood water depth in streets is calculated and used as a risk factor. Remote sensing (RS) and Geographic Information System (GIS) techniques are used for obtaining and preparing input data to the hydraulic model. Different flood scenarios for various return periods were investigated and the flood risk code maps for road network were generated. The results obtained show that 41.2% of road network in the study area is subjected to major flood risk from fairly frequent rainfall events, this percentage reaches 80% to 90% for low frequent flood events (50 year and 100 year flooding). The new approach has been assessed through the comparison of the derived results to the actual flood data and its accuracy reached 77%. The results of this study may help the decision makers to take the necessary actions to protect people and properties.

In this paper we present the importance of landslide susceptibility mapping for territorial planning applications on local and regional transportation networks. The case study is focused on the Timiş-Cerna Valley (Romania), along the Orient-East Mediterranean TEN-T Corridor. We developed the prediction model based on a landslide inventory produced with high-resolution aerial imagery, field study observations and information available in similar geomorphological studies. A selection of determining factors, such as lithology, slope aspect and inclination, fault distance, hydrological parameters and vegetation cover has been cross-referenced with landslide-affected regions in order to draw relevant conclusions. The landslide susceptibility map was based on the relative importance of these parameters, determined by means of cartographic algebra techniques and use of bivariate statistical methods in GIS environment. The results of the prediction analysis, validated with the Receiver Operating Characteristic (ROC) curve, were satisfactory. Finally, the overlay analysis of landslide-susceptible areas and the transportation network provided insight related to the most vulnerable areas at the level of territorial administrative units (TAUs). The importance of landslide zonation map was also featured with respect to the design of high-speed transportation routes across the Timiş-Cerna Corridor.

The structural characteristics of an urban road network directly affect the urban road network's overall function and service level. Because the hierarchical division and layout form of an urban road network has self-similarity and scale invariance, the urban traffic network has certain time-space fractal characteristics, and fractal theory has become a powerful tool for evaluating traffic networks. This paper calculates and compares five fractal dimensions (FD) of nine districts in Harbin. Meanwhile, each calculated FD is linearly regressed with the area, population, built-up area, building area, the total number and length of roads, and the number of buildings in the region. The results show that the fractal dimensions of the five types are between 1 and 2. In the same district, the values of the FD perimeter and FD ruler are lower compared to the FD box, FD information, and FD mass, whereas those of the FD box and FD information are higher. Compared to the FD box and FD information, the value of FD mass shows unevenly. Based on the current research results, this study discusses the feasibility of using relevant indicators in the fractal process to evaluate the layout of the urban road network and guide its optimization and adjustment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

As the Internet of Things devices are deployed on a large scale, location-based services are being increasingly utilized. Among these services, kNN (k-nearest neighbor) queries based on road network constraints have gained importance. This study focuses on the CkNN (continuous k-nearest neighbor) queries for non-uniformly distributed moving objects with large-scale dynamic road network constraints, where CkNN objects are continuously and periodically queried based on their motion evolution. The present CkNN high-concurrency query under the constraints of a super-large road network faces problems, such as high computational cost and low query efficiency. The aim of this study is to ensure high concurrency nearest neighbor query requests while shortening the query response time and reducing global computation costs. To address this issue, we propose the DVTG-Index (Dynamic V-Tree Double-Layer Grid Index), which intelligently adjusts the index granularity by continuously merging and splitting subgraphs as the objects move, thereby filtering unnecessary vertices. Based on DVTG-Index, we further propose the DVTG-CkNN algorithm to calculate the initial kNN query and utilize the existing results to speed up the CkNN query. Finally, extensive experiments on real road networks confirm the superior performance of our proposed method, which has significant practical applications in large-scale dynamic road network constraints with non-uniformly distributed moving objects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tikrit University for Humanities is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

In this work, a conserved higher-order (CHO) macroscopic traffic flow model is extended to road networks. We first introduce the Riemann problem at a junction for the CHO model and then provide a general framework to solve it, considering the compatibility between the CHO and Lighthill-Whitham-Richards (LWR) models. Through the presented framework, Riemann solvers for the LWR model can be extended to the CHO model. Specifically, a kind of Riemann solver for the LWR model is extended to derive the Riemann solver for the CHO model, in which the total actual flow at the junction is maximized. The Riemann solvers for three typical junctions (one-in-two-out junction, two-in-one-out junction, and two-in-two-out junction) are discussed. The first-order finite-volume method is adopted to solve the extended model. Numerical examples are given to validate the extended model and the numerical algorithm. [ABSTRACT FROM AUTHOR]

For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement patterns. Because of spatial heterogeneity, the effective detection of inhomogeneous and irregularly shaped bivariate clusters from bivariate OD movement data remains a challenge. To fill this gap, we propose a network-constrained method for clustering two types of individual OD movements on road networks. To adaptively estimate the densities of inhomogeneous OD movements, we first define a new network-constrained density based on the concept of the shared nearest neighbor. A fast Monte Carlo simulation method is then developed to statistically estimate the density threshold for each type of OD movements. Finally, bivariate clusters are constructed using the density-connectivity mechanism. Experiments on simulated datasets demonstrate that the proposed method outperformed three state-of-the-art methods in identifying inhomogeneous and irregularly shaped bivariate clusters. The proposed method was applied to taxi and ride-hailing service datasets in Xiamen. The identified bivariate clusters successfully reveal competition patterns between taxi and ride-hailing services. [ABSTRACT FROM AUTHOR]

Reasonable districting plays an important role in the patrolling process. In this paper, workload attributes are considered, and a mixed integer programming model is developed to solve the street patrol districting problem (SPDP). The improved spectral clustering algorithm named spectral clustering algorithm based on the road network (SCRn) and simulated annealing algorithm (SA) are combined. This results in a hybrid algorithm called SCRn-SA. The SCRn-SA algorithm is tested on small examples and real instances in Zhengzhou, China. The experimental results show that the proposed algorithm is effective for solving SPDP. It has better performance when compared to other advanced algorithms. [ABSTRACT FROM AUTHOR]

Transportation modes are shifting toward electric vehicles from conventional internal combustion engines to reduce pollution and dependency on conventional fuels. This reduces the fuel cost, while charging stations must be distributed across the locations to minimize range anxiety. Installing charging stations randomly across the distribution system can lead to violation of active power loss, voltage deviation, and reliability parameters of the power system. The problem of the optimal location of charging stations is a nonlinear optimization problem that includes the parameters of the distribution system and road network with their respective constraints. This work proposes a new metaheuristic optimization algorithm, a levy-enhanced opposition-based gradient-based optimizer (LE-OB-GBO), to solve the charging station placement problem. It has a balance between exploration and exploitation and fast convergence rate. The performance of the proposed algorithm was evaluated by solving CEC 2017 benchmark functions and charging station problem. The performance of the proposed algorithm was also compared with that of other state-of-the-art optimization algorithms and was found to outperform 17 out of 29 CEC 2017 functions. Statistical analysis of the charging station placement problem indicates the lowest mean values of 1.4912, 1.4783, and 1.5217 for LE-OB-GBO for considered cases 1 to 3, respectively, thus proving the efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture 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.)

This study focused on pre-flood measures to estimate evacuation times impacted by flood depths and identify alternate routes to reduce loss of life and manage evacuation measures during flood disasters. Evacuation measures, including traffic characteristics, were reviewed according to different flood depths. Several scenarios were constructed for different flooding situations and traffic volumes. Evacuation times in the study area were evaluated and compared for all scenarios with reference to dry conditions. Results of network performance indicators compared to the dry situation showed that average speed dropped to 2 km/h, VHT rose above 200%, and VKT rose above 30%. Cumulative evacuee arrival percentage increased when flood levels were higher than 5 cm. Flood levels of 10–15, 15–20, 20–25, and 25–30 cm represented percentages of remaining evacuees at 9%, 19%, 49%, and 83%, respectively. Time taken to evacuate increased according to flood level. For flood depths of 5–30 cm, travel time increased by 40, 90, 260, and 670 min, respectively, suggesting the need for early evacuation before the flood situation becomes serious. [ABSTRACT FROM AUTHOR]

Scientific analysis of the temporal and spatial characteristics of urban construction land expansion is helpful to realize the sustainable development of cities. In order to explore the development law of construction land and the distribution characteristics of the correlation between construction land expansion and road development more comprehensively, based on the land use / cover change data and OSM (open street map) road network data in Fuzhou, the multi-loop buffer, equal sector, latitude and longitude network were used to divide the research unit, calculated the overall and local fractal dimension, compactness, density, expansion intensity, expansion speed and other expansion indicators of construction land, as well as road network indicators such as road line density and road weighted kernel density, and combined with the bivariate spatial autocorrelation analysis, the spatial and temporal law of the expansion of construction land and its spatial correlation with road development in Fuzhou from 2000 to 2020 were explored. The results show these as follows. The growth rate of construction land area in Fuzhou shows a trend of first increase and then decrease. The main area of construction land expansion is 3 ~ 17 km away from the city center, and has the trend of outward expansion. The expansion intensity of the southeast region is the largest, the expansion speed of the northeast and southwest regions is faster, and the expansion intensity and expansion speed of the western and northern regions are lower. In 2015 and 2020, the distribution of construction land and road network shows a high spatial correlation. Global Moran􀆳 I indices are 0. 829 and 0. 835, respectively. High-high clustering is concentrated in the urban center, and lowlow clustering is concentrated in the western and marginal areas of the city. The urban center and the southeast coastal area are the main areas for the development of construction land and road network, and the development of the two is more coordinated. [ABSTRACT FROM AUTHOR]

The analysis of individuals' movement behaviors is an important area of research in geographic information sciences, with broad applications in smart mobility and transportation systems. Recent advances in information and communication technologies have enabled the collection of vast amounts of mobility data for investigating movement behaviors using trajectory data mining techniques. Trajectory clustering is one commonly used method, but most existing methods require a complete similarity matrix to quantify the similarities among users' trajectories in the dataset. This creates a significant computational overhead for large datasets with many user trajectories. To address this complexity, an efficient clustering-based method for network constraint trajectories is proposed, which can help with transportation planning and reduce traffic congestion on roads. The proposed algorithm is based on spatiotemporal buffering and overlapping operations and involves the following steps: (i) Trajectory preprocessing, which uses an efficient map-matching algorithm to match trajectory points to the road network. (ii) Trajectory segmentation, where a Compressed Linear Reference (CLR) technique is used to convert the discrete 3D trajectories to 2D CLR space. (iii) Spatiotemporal proximity analysis, which calculates a partial similarity matrix using the Longest Common Subsequence similarity indicator in CLR space. (iv) Trajectory clustering, which uses density-based and hierarchical clustering approaches to cluster the trajectories. To verify the proposed clustering-based method, a case study is carried out using real trajectories from the GeoLife project of Microsoft Research Asia. The case study results demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art clustering-based methods. [ABSTRACT FROM AUTHOR]

The widespread use of mobile devices has resulted in the generation of vast amounts of spatial data. The availability of such large-scale spatial data facilitates the development of data-driven approaches to address real-life problems. This paper introduces the max coverage region (MCR) problem in road networks and provides efficient solutions. Given a set of spatial objects and a coverage radius, the MCR problem aims to identify a location from the road network, so that we can reach as many spatial objects as possible within the given coverage radius from the location. This problem is fundamental to supporting many real-world applications. Given a road network and a set of sensors, this problem can be used to find the best location for a sensor maintenance station. This problem can also be applied in medical research, such as in a protein–protein interaction network, where the nodes represent proteins, the edges represent their interactions, and the weight of an edge represents confidence. We can use the MCR problem to find the set of interacting proteins with a confidence budget. We propose an efficient exact solution to solve the problem, where we reduce the MCR problem to an equivalent problem named the most overlapped interval and design an edge-level upper bound estimation method to reduce the search space. Furthermore, we propose two approximate solutions that sacrifice a little accuracy for much better efficiency. Our experimental study on real-road network datasets demonstrates the effectiveness and superiority of the proposed approaches. [ABSTRACT FROM AUTHOR]

Linear feature networks are the roads, trails, pipelines, and seismic lines developed throughout many commercial boreal forests. These linear features, while providing access for industrial, recreational, silvicultural, and fire management operations, also have environmental implications which involve both the active and non-active portions of the network. Management of the existing linear feature networks across boreal forests would lead to the optimization of maintenance and construction costs as well as the minimization of the cumulative environmental effects of the anthropogenic linear footprint. Remote sensing data and predictive modelling are valuable support tools for the multi-level management of this network by providing accurate and detailed quantitative information aiming to assess linear feature conditions (e.g., deterioration and vegetation characteristic dynamics). However, the potential of remote sensing datasets to improve knowledge of fine-scale vegetation characteristic dynamics within forest roads has not been fully explored. This study investigated the use of high-spatial resolution (1 m), airborne LiDAR, terrain, climatic, and field survey data, aiming to provide information on vegetation characteristic dynamics within forest roads by (i) developing a predictive model for the characterization of the LiDAR-CHM vegetation cover dynamic (response metric) and (ii) investigating causal factors driving the vegetation cover dynamic using LiDAR (topography: slope, TWI, hillshade, and orientation), Sentinel-2 optical imagery (NDVI), climate databases (sunlight and wind speed), and field inventory (clearing width and years post-clearing). For these purposes, we evaluated and compared the performance of ordinary least squares (OLS) and machine learning (ML) regression approaches commonly used in ecological modelling—multiple linear regression (mlr), multivariate adaptive regression splines (mars), generalized additive model (gam), k-nearest neighbors (knn), gradient boosting machines (gbm), and random forests (rf). We validated our models' results using an error metric—root mean square error (RMSE)—and a goodness-of-fit metric—coefficient of determination (R2). The predictions were tested using stratified cross-validation and were validated against an independent dataset. Our findings revealed that the rf model showed the most accurate results (cross-validation: R2 = 0.69, RMSE = 18.69%, validation against an independent dataset: R2 = 0.62, RMSE = 20.29%). The most informative factors were clearing width, which had the strongest negative effect, suggesting the underlying influence of disturbance legacies, and years post-clearing, which had a positive effect on the vegetation cover dynamic. Our long-term predictions suggest that a timeframe of no less than 20 years is expected for both wide- and narrow-width roads to exhibit ~50% and ~80% vegetation cover, respectively. This study has improved our understanding of fine-scale vegetation dynamics around forest roads, both qualitatively and quantitatively. The information from the predictive model is useful for both the short- and long-term management of the existing network. Furthermore, the study demonstrates that spatially explicit models using LiDAR data are reliable tools for assessing vegetation dynamics around forest roads. It provides avenues for further research and the potential to integrate this quantitative approach with other linear feature studies. An improved knowledge of vegetation dynamic patterns on linear features can help support sustainable forest management. [ABSTRACT FROM AUTHOR]

In this work, a method for the fast evaluation of seismically-induced landslides is applied at regional scale in the Granada Basin (southern Spain). The methodology is based on the concept of rigid block but takes into account the variability in input data (geotechnical properties, failure depth and saturation state of the materials) through the use of a logical tree scheme. Input data were combined and evaluated using five different Newmark displacement empirical relations, which are automatically weighted to obtain a final co-seismic landslide susceptibility map. A Geographic Information System system was used to obtain the resulting maps considering the different branches and weights of the logic tree through an specifically developed automatic code written in Phyton. The landslides induced by the 2021 Mw 4.4 events of the Santa Fe seismic series have been used to validate the methodology. This is later used as input along with the road network to analyze the distribution of areas of different susceptibility level. The results obtained show that 1.73% of the total area is characterized by moderate to high susceptibility, being the southern and eastern sections of the Granada basin were more vulnerable slopes are found. When considering only zones around the road network (50 m buffer), it is found that 2.11% of slopes around highways and 1.33% of road slopes have moderate to high susceptibility, especially the A-92 highway and the A-338 road. These roads traverse areas with steep slopes and a combination of hard and soft materials, which increases the susceptibility of disrupted landslides. On the other hand, in more densely populated areas such as the metropolitan area of Granada, the susceptibility is lower due to their location on terrain with low to negligible slopes. The results are of interest for authorities managing slope stabilities because they allow establishing effective and locally-based seismic forecast works in order to minimize the damage of future events.

Historical data on land cover/use and road networks are important not only for cultural heritage preservation in the context of digital humanities, but also for understanding the evolution of landscapes and human infrastructures aimed at efficient management of land systems. In this manuscript, we present a spatial database that contains basic background layers of the island of Cyprus in the 1960s. These data are derived from the topographic map of Cyprus produced in the 1960s and published in 1969. They were acquired after the digitization of the K715 map series (1:50,000) of the Corps of Engineers of the U.S. Army Map Service (hereafter ''K715 map'') [1]. The database consists of the following vector layers: a) land use/land cover, b) road network, c) coastline, d) settlements, and covers the entire area of the island (9,251 km2). The road network is divided into six categories and the land use/land cover into thirty-three different types according to the legend of the original map. In addition, the 1960 census was included in the database to assign population data to settlement entities (towns or villages). This census was the last census of the total population under the same authority and method, since Cyprus was divided into two parts five years after the map was published and as a result of the Turkish invasion. Therefore, the dataset can be used not only for cultural and historical preservation purposes, but also to measure the different development of the landscapes that fell under a different political status since 1974.

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.

Amidst the intensifying extreme rainfall patterns due to climate change, global early warning systems for mass movements (e.g. landslides, avalanches) need to provide not only the coarsely aggregated danger reports, but also the necessary fine details to understand its potential implications on critical infrastructures such as transportation systems. In this study, we introduce a novel ‘intergraph’ method that enhances the exposure information using a graph-based machine learning implementation on the hydrological and geological characteristics of mass movements and the underlying connectivity of settlement-transportation systems. Demonstrating the entire country of Norway and the 2020 Gjerdrum quick clay incident as a case study, we integrated the assessment of both direct and indirect exposure information of settlement-transportation systems and their daily 1 km-by-1 km susceptibility map, which were derived from the 68 934 mass movement incidents since 1957 and the connectivity information of 4778 settlements and 257 000 km road networks. Our findings achieved 86.25% accuracy, providing a distribution of improved susceptibility estimates and identifying critical settlements in near-real-time. By interacting the graphical representations of the shared causal drivers of susceptibility and the settlement-transportation system connectivity, our study extends our understanding of the exposure of multiple interacting settlements with a high granularity degree in a unified approach.

Abstract The information of road intersection and traffic rules plays an essential role in transportation system especially for people travelling to large, unfamiliar cites. Moreover, cities are growing fast and it is difficult to keep up with the changing frequency of road network information although road map is periodically updated to provide reliable information. In order to tackle this problem, an automatic method to detect road intersection and traffic rules using a floating car trajectory dataset is proposed. The turning trajectories using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules were first extracted. Then a Two‐trajectory Intersect Angle‐based (TIA) method was proposed to locate the candidate points at road intersection. Finally the intersections (junctions) were detected using a cluster‐based technique. Furthermore, turning rules at road intersections were extracted automatically by clustering the angles both before and after making a turn. The effectiveness of our method was demonstrated using big floating car trajectory dataset collected in Fuzhou, China. Additional comparisons and analysis were also conducted to confirm identification results. Our findings showed that a total 333 at‐grade intersections were detected automatically with more than 94% in accuracy, and turning rules identification reached 85% in accuracy by manually comparing the results.

In this paper, it is proposed to carry out the assessment of road transport hazards using the minimization of the negative impact by three main components: impact of changes in emissions of toxic elements of pollutants from the exhaust gases of car engines, change of noise pollution of the environment and change of the number of accidents. The method of determination of equivalent losses which will be as a result of the release of a conventional ton of toxic components of pollutants from the exhaust gases of car engines and the impact of noise pollution of the environment on three groups of components – drivers and passengers, pedestrians and residents of adjacent territories is justified. Special attention is paid to determining equivalent losses due to traffic accidents with injured or dead. For example, a study to determine the change in environmental hazards of road transport for the section of Lubinska Str. in Lviv, depending on the main indicator of traffic flow - the speed of traffic was carried out. It is established that for the speed of 25 km/h the minimum environmental damage will be 1093 thousand UAH per year, and the minimum total hazards of road transport, taking into account the possibility of an accident at 12 km/h will be 1239 thousand UAH per year. With the help of the obtained model, it is possible to determine the amount of hazards from road transport, which allows taking into account environmental, social and economic components when studying the levels of the negative impact of transport on the environment and ensuring minimum accident rates on the studied section of the road network. Conducting preliminary theoretical research to find rational solutions when applying schemes to improve traffic organization will be especially useful.

Abstract Urban landscapes often harbour organisms that harm people and threaten native biodiversity. These landscapes are characterized by differences in socioeconomic context, habitat suitability and patch connectedness. Identifying which spatial differences enable outbreaks of pests, pathogens and invasive species will improve targeted control efforts. We tested hypotheses to explain the distribution and demography of puncturevine Tribulus terrestris, a human‐dispersed invasive plant in Boise, a city in the western United States. We hypothesized an increase in puncturevine infestations near low‐valued properties with a high proportion of bare ground, the species' preferred microhabitat, that are well connected on the urban road network. To test these hypotheses, we collected data on the abundance, emergence and persistence of reproductive plants in transects spanning >100 km of our study city. We then used hierarchical Bayesian models to evaluate the impacts of spatial covariates on puncturevine distribution and demography. Bare ground cover consistently increased abundance, emergence and persistence of puncturevine, indicating the overarching importance of suitable establishment sites for this invasive species. Property value had the strongest impact on puncturevine abundance and was the most important main effect in the model for puncturevine emergence. In both models, lower‐valued properties had a higher risk of puncturevine occurrence. The effects of road network connectivity depended on bare ground cover, with the highest predicted abundance and emergence of puncturevine in patches with low connectivity on the road network and high bare ground cover. Understanding these relationships will require data that can disentangle seed dispersal from establishment limitations. Identifying characteristics of urban patches that promote outbreaks of harmful species will improve the effectiveness and equity of control efforts. The prevalence of puncturevine in lower‐valued properties is cause for concern as the plant impedes bicycle transportation, with the potential for disproportionate impacts on marginalized groups that lack resources to repair punctured tires. Our results add to a growing recognition that low‐connectivity street networks have undesirable outcomes for environmental sustainability. As urban land cover continues to grow worldwide, accounting for interactions between human, natural and built systems will play a crucial role in managing invasive species.

Effective techniques for reducing traffic congestion include adding new edges to the road network or increasing the capacity of existing edges. To determine the optimal set of improvements that can be made to an existing road network, it is standard practise to solve a Discrete Network Design Problem. Due to the bi-level nature of its formulation, it is one of the most difficult problems to solve to optimality. The Bacterial Foraging Optimization technique has been investigated in a previous work to gauge its effectiveness in determining the n-optimal solution to the Discrete Network Design Problem. This concept is further developed in this paper by hybridizing the Bacterial Foraging Optimization algorithm with Simulated Annealing. In the elimination step, the metropolis criterion from Simulated Annealing ensures a more diverse search of the solution space. We also employ a modified candidate set of improvements that incorporates both edge and capacity additions. The effectiveness of the hybrid bacterial foraging optimization algorithm in determining n-optimal solutions to the Discrete Network Design Problem has been demonstrated by testing it on the 24-node, 76-arc Sioux Falls network.

Traffic congestion is a significant problem affecting the sustainable development of urban traffic. It is important to analyze the congestion and forecast future traffic models to prevent traffic congestion. This study is conducted with the main aim to determine the most congested area of the road network and determine how they are related to the demand of the drivers. This study uses the Floating Car Data method to find the traffic congestion and the degree to which observed congestion clusters are a meaningful representation of congestion patterns within a more extensive urban road network. Statistical calculations have been carried out to determine the correlation between clusters based on which conclusions are drawn. Findings have shown that this approach can effectively identify the traffic congestion patterns in the urban road network. The analyses of the traffic congestion behaviour have shown that congestion is more severe and widespread in evening rush hours than morning. Overall, the results can be used to develop a framework to describe potential traffic issues and a system for predicting congestion.

As the backbone and the 'blood vessel' of modern cities, road networks provide critical support for community activities and economic growth, with their roles even more crucial due to the dramatic progress in urbanization. The service of road networks is subjected to the increasing frequency of high-consequence natural hazards such as earthquakes, floods, hurricanes, etc. Identifying resilient restoration sequences is essential to mitigate the disruption of such important infrastructure networks. This paper investigates a novel decision-support model to optimize post-disaster road network repair sequence. The model, named as GCN-DRL model, integrates the advantages of deep reinforced learning (DRL) with graph convolutional neural network (GCN), two emerging artificial intelligence (AI) techniques to achieve efficient recovery of road network service. The model is applied to analyze two cases of community road networks in the US that are subjected to different types of hazards, i.e., earthquakes and flooding. The performance of repair sequence by the GCN-DRL model is compared with two commonly used methods, i.e., repair sequence by the genetic algorithm and by prioritization based on graph importance with betweenness centrality. The results showed the decision sequence by GCN-DRL model consistently achieved superior performance in road network restoration than the conventional methods. The AI-based decision model also features high computational efficiency since the GCN-DRL model can be trained before the hazard. With a pre-trained GCN-DRL model, a close to optimal decision-making process can be made available rapidly for different types of new hazards, which is advantageous in efficiently responding to hazards when they happen. This study demonstrates the promise of a new AI-based decision support model to improve the resilience of road networks by enabling efficient post-hazards recovery. [ABSTRACT FROM AUTHOR]

Ride sharing is a service that enables users to share trips with others, conserving energy, decreasing emissions and reducing traffic congestion. Selecting a suitable partner for a user based on the their trip data is essential for the service, but it also leads to privacy disclosure, e.g., the user's location and trajectory. Many privacy-preserving solutions for ride sharing services have been proposed, which are based on cryptographic technology and provide accurate matching services. However, these encryption-based algorithms are very complicated and difficult to calculate. In hot spots, such as stations, airports and sport gymnasiums, a large number of users may apply for a ride sharing service in short space of time, which will place huge pressure on the service provider. Using traditional matching methods increases the matching time and leads to a less favorable user experience. To solve these problems, we model them, aiming to maximize the vehicle's carrying capacity and propose a lightweight privacy-preserving ride matching scheme for selecting feasible partners during busy periods with a large number of requests. To achieve this, we make use of the homomorphic encryption technique to hide location data and design a scheme to calculate the distances between users in road networks securely and efficiently. We employ a road network embedding technique to calculate the distance between users. Moreover, we use travel time instead of space distance, which makes matching more accurate. Further, with the encrypted itineraries of users, the service provider selects potential ride share partners according to the feasibility of time schedules. We use ciphertext packing to reduce overhead, improving the efficiency of ride matching. Finally, we evaluate our scheme with simulation and demonstrate that our scheme achieves an efficient and accurate matching service. It only takes a few seconds to complete the matching, and the matching accuracy is higher than 85 percent in most cases. [ABSTRACT FROM AUTHOR]

Infrastructures (viaducts, bridges, tunnels, railways, and roads) are fundamental to allow people and goods move around a country. In particular, in Italy, due to its orographic and morphological complexity, the ratio between the number of viaducts and the national geographical extension is the largest in Europe. This kind of structure constitutes a weakness of the road networks, especially those over 50 years old, and among these, prestressed concrete structures are the most vulnerable. Therefore, the study of the robustness of an infrastructure network is a quite useful topic for modern societies and public mobility managers. The aim of the present work is to develop a reliable and fast analysis tool capable of processing many structures in a relatively short time. In order to calibrate the most suitable instrument, starting from the study of an experimental campaign on six prototypes of artificially corroded posttensioned concrete beams, the reliability of concentrated plasticity models was investigated to reproduce force‐displacement diagrams and in particular, the effect of corrosion on the moment‐curvature relationship. In fact, this method could provide a well‐defined nonlinear analysis of prestressed elements, especially in the case of grillage decks where external girders could be more affected by corrosion damage in opposition to internal girders or diaphragms. A numerical model by FEM analysis is also presented to reproduce a vulnerability study on an actual structure. [ABSTRACT FROM AUTHOR]

Multi-source skyline query based on road network distance is widely used in map services. However, the existing skyline query methods are inefficient for complex road network distance calculation, and with the increasing of the number of the query point, the query result set becomes too large to provide users with tidy and effective query results. In order to improve the validity of the query results and the query efficiency, this paper proposed an inverted index skyline query algorithm based on the minimum aggregation distance, which established QG-tree index for road network to improve the calculation efficiency of aggregation distance. And it established an inverted index for the points of interest set to retrieve the points of interest combined with pruning strategy, which reduced the overhead of aggregation distance calculation and the dominantion determination, and effectively improved the query efficiency. Finally, experiments on the real road network show that the efficiency of the proposed algorithm is 1～3 orders of magnitude faster than that of the existing algorithms DSR and N3 S, and it can effectively handle the multi-source skyline query problem in the road network. [ABSTRACT FROM AUTHOR]

Over the past decades, China has built a large number of superblocks and gated residential areas, which have contributed to increased congestion on urban road traffic. Although some studies have found a correlation between residential environments and travel convenience, there is little quantitative evidence to explain which factors influence the connectivity of road networks. This paper aims to clarify the indicators of built environments in Chinese residential areas that affect travel accessibility and their correlation proportions through a more quantitative method and to provide a basis for planning and design decisions. To this end, this study took 204 residential areas in the three districts of Nanjing, China as a sample and calculated 16 built-environment indicators. The path distance (D) and pedestrian route directness (PRD) from the center of these residential areas to the nearest urban intersection were measured by GIS as outcome variables. First, correlation analysis was used to screen for significant variables, and multiple linear regression models were used to examine the significant influencing factors of D and PRD. Then, a binary logistic regression analysis was performed to provide a model that could determine whether the built environment of the residential area met the requirements for convenient travel. The results revealed that the length of the long side, the area size, and the total road length of the residential area were significantly related to D, and the number of entrances and exits, the intersection density, and the X ratio were significantly related to PRD. The indicators that were positively correlated with D were the size, network complexity, and the boundary shape of the residential area according to the degree of correlation. Meanwhile, other indicators such as the density and connectivity of the residential road network were negatively correlated with D. [ABSTRACT FROM AUTHOR]

An intelligent, accurate, and powerful object detection system is required for automated driving systems to keep these vehicles aware of their surrounding objects. Thus, vehicles adapt their speed and operations to avoid crashing with the existing objects and follow the driving rules around the existence of emergency vehicles and installed traffic signs. The objects considered in this work are summarized by regular vehicles, big trucks, emergency vehicles, pedestrians, bicycles, traffic lights, and traffic signs on the roadside. Autonomous vehicles are equipped with high-quality sensors and cameras, LiDAR, radars, and GPS tracking systems that help to detect existing objects, identify them, and determine their exact locations. However, these tools are costly and require regular maintenance. This work aims to develop an intelligent object classification mechanism for autonomous vehicles. The proposed mechanism uses machine learning technology to predict the existence of investigated objects over the road network early. We use different datasets to evaluate the performance of the proposed mechanism. Accuracy, Precision, F1-Score, G-Mean, and Recall are the measures considered in the experiments. Moreover, the proposed object classification mechanism is compared to other selected previous techniques in this field. The results show that grouping the dataset based on their mobility nature before applying the classification task improved the results for most of the algorithms, especially for vehicle detection. [ABSTRACT FROM AUTHOR]

Today, human activities, comings and goings and communications at the urban and rural levels have changed a lot and these changes have been regardless of environmental conditions and the desired human environment. The present research is based on applied development goal and in terms of descriptive-analytical methodology through library and field studies. To study, the optimal model for the development of rural roads network in Shahinshahr and Meimeh was employed. To achieve the objectives of the research, 4 indicators: natural and ecological, socio-cultural, economic and physical-spatial were extracted and surveyed by 30 experts in the field of urban and rural studies by using Smart- Pls software to analyze the factors. Exploration was done and then, in order to present the optimal model for the development of the rural road network, the colonial competition algorithm (MST minimum tree cover) in Matlab 2016 software environment has been used. Tracking Analyst Tools method in spatialization of the studied indicators in Network Analyst Tools is used in the ArcGIS software environment. The results show that among the studied indicators, the socio-cultural index has the highest coefficient and impact on the development of rural road network and then the economic index is effective as a stimulus and mediator in the second category. The results of spatial analysis also show that among the studied villages, 66.6% are in unsuitable and completely unsuitable situation in terms of the development of the road network. [ABSTRACT FROM AUTHOR]

Exploring the land surface landscape fragmentation and vegetation loss caused by natural and human factors in the hilly and gully region of the Loess Plateau can provide scientific basis for economic development and environmental protection in poor mountainous areas. Based on 2018 Google Earth and Landsat OLI optical remote sensing imagery, using buffer analysis, overlay analysis, landscape index analysis and other geospatial analysis methods, the landscape fragmentation and vegetation index loss in the Zijin Mountain area caused by road and residential construction were quantitatively described. The results are as follows. (1) The bottom slope of the valley in the study area was less than 8° and quite different from the both sides slope of the valley. The landscape was cut by the valley, and the direct erosion area accounted for 6.19% of the total area. (2) In 2018, the road density in the Zijin Mountain area was 0.70 km·km-2. While the transportation was convenient, it stimulated the expansion of surrounding residential areas, resulted in 4.01% of the area being directly occupied by roads and residential areas. (3) 64.87% of the area in the study area was indirectly affected by human activities. Roads and residential areas cut the landscape into 249 patches, and the degree of landscape fragmentation was extremely high. Only 10.44% of the patches were suitable as habitats for small and medium-sized wildlife. (4) In 2018, 18.09% of the NDVI in the Zijin Mountain area was jointly affected by valley erosion and human activities, and the spatial difference of vegetation coverage was significant. The surface landscape in the Zijin Mountain area was mainly severely disturbed by human activities, resulted in certain impacts on the wildlife habitat and NDVI. [ABSTRACT FROM AUTHOR]

Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around 0.6 ) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation ρ ≈ 0.4 at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM AUTHOR]

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

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

Forest road planning with the available tools, e.g. PEGGER and GIS, still requires a lot of time of an expert, and the designed roads are not guaranteed to be efficient in terms of the cost or suitability of the road. In this article, we propose a novel Genetic Algorithm (GA) based method for forest road planning. To do so, each road is represented as a sequence of fixed and variable (control) points. A novel objective (fitness) function is defined based on the length, gradient, and suitability of the roads (individuals). The proposed algorithm is applied to the Arasbaran forest area and the resulted roads are compared with PEGGER-designed roads regarding length, Bachmund index, accessibility, and suitability. The results clearly show the power of the proposed GA algorithm in reducing computation time, road construction costs, and environmental impacts compared to the common road planning approaches. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have attracted considerable attention because of their clean and high-energy efficiency. Reasonably planning a charging station network has become a vital issue for the popularization of EVs. Current research on optimizing charging station networks focuses on the role of stations in a local scope. However, spatial features between charging stations are not considered. This paper proposes a charging station selection method based on the graph convolutional network (GCN) and establishes a charging station selection method considering traffic information and investment cost. The method uses the GCN to extract charging stations. The charging demand of each candidate station is calculated through the traffic flow information to optimize the location of charging stations. Finally, the cost of the charging station network is evaluated. A case study on charging station selection shows that the method can solve the EV charging station location problem. [ABSTRACT FROM AUTHOR]

The research examines the implication of Nigeria's lopsided land transport structure for performance and efficiency in the freight transport sector using the Lagos–Kano corridor as a case study. A comparative analysis of road and rail performance in freight transport using defined performance indicators shows that road transport performed better on all but two indicators, namely, freight ton-kilometres moved per vehicle hour travelled and throughput per unit of energy consumed. Other performance indicators such as absolute throughput of freight, freight ton-kilometres performed, speed, freight throughput per unit cost of operation and revenue per ton of freight moved favoured roads over rail. Clearly, rail comparative advantage relates to capacity, but freight shippers are looking for speed that shortens vehicle turnaround time. The lesson is that freight shippers will not explore modal comparative advantages (such as capacity and lower freight rates) except such advantages are complemented with competitiveness conferred by technology (especially speed). [ABSTRACT FROM AUTHOR]

Road infrastructure, an essential support system providing mobility and accessibility within residential layouts, has become a serious concern in several countries. In some places, residential layouts (subdivisions) have been developed through private ownership. If the norms laid down by central development authorities are not followed by local developers, land use, including road infrastructure, is affected. This study presents a quantitative assessment of the impact of improper development of urban residential layouts on road infrastructure. The study developed an index for assessing the road infrastructure within residential layouts based on identifying the deficiencies which affect mobility and accessibility. A road infrastructure development index (RIDI) was conceptualized using an analytical hierarchy process (AHP) and parameters that affect accessibility and commuter comfort in residential layouts derived from high-resolution satellite data. The parameters used to generate this RIDI were the number of approach roads, spatial road serviceability, road density, bottlenecks, population density, dead ends or cul-de-sacs, and road width. Sample layouts were selected from varying built-up land-use classes to extract the index parameters to develop the RIDI. The RIDI values estimated for sample layouts indicate the level of development of road infrastructure in the residential layouts. A case study of the Nagpur urban area in central India, showing estimated RIDI values, is discussed. The RIDI concept can be used for proper planning, monitoring, approvals, and improvement of road infrastructure in residential layouts in urban areas, especially in the developing countries, after its calibration for varying urban scenarios. [ABSTRACT FROM AUTHOR]

Introduction. To study the urbanized territory as a system, an approach was used that allows assessing gaseous and dispersed atmospheric pollution using a unified scale of environmental hazard. The features of air pollution in urban areas were considered, where a significant number of stationary and mobile sources of negative environmental impact are concentrated in limited overpopulated areas.The purpose of the study. the use of theoretical foundations for a systematic assessment of the level of environmental hazard of an urbanized area under the influence of the road network due to a combination of gaseous and dispersed atmospheric pollution.Methods and materials. Based on the improved model of the Urbanized Territory system, the following hypothesis is proposed: 1) motor traffic flows are both independent sources of formation of dispersed particles and sources of uplifting and spreading of particles, which in the presence of gaseous pollutants increases the level of environmental hazard of atmospheric pollution in an urbanized area; 2) atmospheric pollution of an urbanized area is formed when gaseous pollutants and dispersed particles from the street and road network are superimposed on the polluting background of other elements of the Urbanized territory system.Results. The distribution maps of the calculated complex index of atmospheric pollution were obtained for the most problematic areas of the urbanized territory of Naberezhnye Chelny - Prospekt Mira and Kazansky Prospekt. The obtained results characterize the change in the level of environmental hazard of urbanized areas when gaseous pollutants and dispersed particles from the street and road network are superimposed on the polluting background of other elements of the Urbanized territory system.Discussion and conclusion. Based on the generalization and comparison of the results of previous and current studies of the authors, comparing them with the results of other researchers, the hypothesis was fully confirmed. Conclusion. With the confirmation of the hypothesis, the goal of the study was achieved, which has scientific novelty and practical significance for the arrangement of a favorable and comfortable urban environment.

Cooperative Vehicle Infrastructure Systems (CVIS) play a crucial role in promoting sustainable transportation. Existing studies have primarily focused on the theoretical algorithms of CVIS, with insufficient research on energy consumption evaluation using practical data, particularly under diverse traffic conditions. This study addresses this gap by collecting long-term sequential application data (2.19 million) of CVIS across a wide region, analyzing the performance trends of CVIS under various traffic conditions, and offering practical policy recommendations for optimizing and deploying CVIS effectively. The data from drivers using CVIS are categorized into passive CVIS (P-CVIS) or active CVIS (A-CVIS) based on the utilization of the speed guidance function. The study reveals that drivers with insufficient proficiency in A-CVIS tend to engage in more aggressive driving behaviors, characterized by higher acceleration rates and greater variability in acceleration distribution in response to recommended speeds. These behaviors lead to increased acceleration energy consumption for both vehicles (2.7 % and 9.0 %) and road networks (65.2 % and 24.7 %) during peak and off-peak hours, respectively. While A-CVIS can still play an optimizing role during peak hours, the majority of optimization metrics are slightly lower compared to off-peak periods. The study concludes by offering recommendations and regulatory measures to enhance A-CVIS. • Cooperative Vehicle Infrastructure Systems (CVIS) improve road network efficiency. • Active CVIS increases energy consumption during peak and off-peak hours. • Active CVIS's performance is slightly lower during peak hours than off-peak hours. • Improving driver proficiency is essential for enabling active CVIS functionality. [ABSTRACT FROM AUTHOR]

• CO 2 emission intensity based on length of road network is effective indicator. • CEI for passenger traffic shows stronger spatial heterogeneity than freight. • The driving effect of the road network on passenger traffic CEI varies spatially. • Industrial land proportion dominates CEI for freight traffic. • Discussed and proposed differentiated carbon reduction policy implications. Road traffic is a major contributor to global carbon dioxide emissions. The impact of urban road networks and land use on these emissions remains controversial. This study proposed carbon dioxide emission intensity (CEI) based on road network length, analyzed the spatial distribution characteristics of CEI of passenger and freight traffic in the Southern Kanto region of Japan through spatial autocorrelation analysis, and examined the driving effects of urban road network and land use characteristics through Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The results showed that the CEI of passenger traffic demonstrated more significant spatial heterogeneity than freight traffic, exhibiting a spatial pattern of high-value clustering in the center and low-value clustering in the peripheral area. Road network characteristics are the primary driving factor for CEI of passenger traffic. A road network with lower density, rich branching streets does not necessarily promote the reduction of carbon dioxide emissions. The industrial land proportion exerts a stronger and more extensive driving effect on the CEI of freight traffic than road network characteristics. As a specialized study on carbon dioxide emissions from road traffic, this study provides the decision-making criteria for urban planning strategy and urban management to enhance urban sustainable development. [Display omitted] [ABSTRACT FROM AUTHOR]

• Delayed and persistent travel speed reduction in post-earthquake scenarios. • Fluctuations of traffic flow around normal operation and scale-free property of link resilience distribution. • Congestion spread from the epicenter, leading to increased travel times in surrounding cities. • Post-earthquake link resilience exhibits spatial autocorrelation. An accurate assessment of the spatial and temporal changes in the post-earthquake road network is essential for scientifically establishing emergency rescue programs and recovery strategies. This paper utilizes high-resolution traffic data to evaluate the resilience of city-to-city road networks to earthquakes. Additionally, it elucidates the distribution of link-level resilience in both temporal and spatial dimensions. Through a case study of the Luding earthquake, the results indicate that the road network's recovery duration exceeds five days, emphasizing the persistent and temporally delayed impact of earthquakes on traffic flow. Specifically, earthquakes disrupt link resilience, causing it to fluctuate around normal operational behavior. In the immediate aftermath of the earthquake, travel time from the epicenter to Luding surged by 132 %, triggering congestion that spread to neighboring cities. At the spatial level, spatial autocorrelation in link resilience is evident, with concentrated clusters of low-resilience links observed in segments such as G248 and S211. Our findings offer valuable and novel insights into the resilience of road networks to earthquakes from an empirical perspective. [ABSTRACT FROM AUTHOR]

Accurately predicting the carbon emissions of urban street vehicles is a current challenge in the field of urban transportation. This study proposed a new SAGE-GSAN model (Graph SAmple and aggreGatE - Graph Spatial Attention Network) to solve this problem. It combines graph neural networks with streetscape features and road network structure to predict the traffic carbon monoxide emissions at the street level. The method takes the street view images, the 5075 roads network structure in Wuhan and 19 street visual elements as the input features, and the carbon monoxide emissions obtained from the driving trajectories as the prediction data. The method achieved an experimental accuracy of 81.4% in predicting carbon monoxide emissions from street cabs. This study also compares the prediction results of traditional neural networks and analyzes the effects of different street-level features and graph convolution layers on the prediction accuracy. The results of this study show that the graph neural network and attention mechanism techniques could solve the fine-grained carbon emission prediction problem at the urban street level effectively. The model code is shared at the: https://github.com/zou9229/CO_Predict_Code. • Using micro-factor to calculate CO emissions from urban street taxis. • Convert road network structure to graph structure. • Proposed a new SAGE-GSAN model (Graph SAmple and aggreGatE - Graph Spatial Attention Network). • Predicting street carbon emission levels using graph neural networks. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the effectiveness of regional disaster mitigation policies in providing equitable access to essential services during hurricane-induced floods in Southeast Louisiana. Studies have been conducted on access to essential service facilities during and after disasters. However, there is limited research on whether current regional disaster mitigation plans ensure equitable access to emergency facilities. To this end, a methodology was developed to assess the effect of the 2017 Louisiana Coastal Master Plan developed by the Coastal Protection and Restoration Agency (CPRA) on equity in connectivity to essential service facilities (hospitals and fire stations) in a case study region in southeastern Louisiana. First, a graph-based model was used to represent the road network and determine the connectivity of five demographic groups during hurricane-induced flood scenarios. Herein, a hurricane-induced flood scenario with a low return period (50 years) was chosen for future years up to 2065, considering sea level rise and subsidence. Roads with flood depths exceeding 30 cm were considered impassable and were removed from the network. Connectivity was then determined using breadth-first search. Monte Carlo simulations were performed to propagate uncertainties in flood depths. It was found that minority groups were at a higher risk of losing access to emergency services during the selected hazard scenario. Native American and Asian populations were found to be at the highest risk of disconnection, followed by the Hispanic and African-American populations. Overall, the coastal master plan did not affect the equity (or lack thereof) in connectivity to essential service facilities. [ABSTRACT FROM AUTHOR]

Measuring bivariate spatial association plays a key role in understanding the spatial relationships between two types of geographical flow (hereafter referred to as "flow"). However, existing studies usually use multiple scales to analyze bivariate associations of flows, leading to the results at larger scales can be strongly affected by the results at smaller scales. Moreover, the planar space assumption of most existing studies is unsuitable for network-constrained flows. To solve these problems, a network incremental flow cross K-function (NIFK) is developed in this study by extending the cross K-function for points into a flow context. Specifically, two versions of NIFK were developed in this study: the global version to check whether bivariate associations exist in the whole study area and the local version to identify specific locations where associations occur. Experiments on three simulated datasets demonstrate the advantages of the proposed method over an available alternative method. A case study conducted using Xiamen taxi and ride-hailing service datasets demonstrates the usefulness of the proposed method. The detected bivariate spatial association provides deep insights for understanding the competition between taxi services and ride-hailing services. [ABSTRACT FROM AUTHOR]