Relevant bibliographies by topics / Road network pattern

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Road network pattern'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Road network pattern"

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Wang, Shiguang, Dexin Yu, Mei-Po Kwan, Huxing Zhou, Yongxing Li, and Hongzhi Miao. "The Evolution and Growth Patterns of the Road Network in a Medium-Sized Developing City: A Historical Investigation of Changchun, China, from 1912 to 2017." Sustainability 11, no. 19 (September 26, 2019): 5307. http://dx.doi.org/10.3390/su11195307.

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Understanding the evolution and growth patterns of urban road networks helps to design an efficient and sustainable transport network. The paper proposed a general study framework and analytical workflow based on network theory that could be applied to almost any city to analyze the temporal evolution of road networks. The main tasks follow three steps: vector road network drawing, topology graph generation, and measure classification. Considering data availability and the limitations of existing studies, we took Changchun, China, a middle-sized developing city that is seldom reported in existing studies, as the study area. The research results of Changchun (1912–2017) show the road networks sprawled and densified over time, and the evolution patterns depend on the historical periods and urban planning modes. The evolution of network scales exhibits significant correlation; the population in the city is well correlated with the total road length and car ownership. Each network index also presents specific rules. All road networks are small-world networks, and the arterial roads have been consistent over time; however, the core area changes within the adjacent range but is generally far from the old city. More importantly, we found the correlation between structure and function of the urban road networks in terms of the temporal evolution. However, the temporal evolution pattern shows the correlation varies over time or planning modes, which had not been reported
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Yang, Weiping. "AUTOMATIC CONSTRUCTION OF HIERARCHICAL ROAD NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 37–44. http://dx.doi.org/10.5194/isprsannals-iii-2-37-2016.

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This paper describes an automated method of constructing a hierarchical road network given a single dataset, without the presence of thematic attributes. The method is based on a pattern graph which maintains nodes and paths as junctions and through-traffic roads. The hierarchy is formed incrementally in a top-down fashion for highways, ramps, and major roads directly connected to ramps; and bottom-up for the rest of major and minor roads. Through reasoning and analysis, ramps are identified as unique characteristics for recognizing and assembling high speed roads. The method makes distinctions on the types of ramps by articulating their connection patterns with highways. Major and minor roads will be identified by both quantitative and qualitative analysis of spatial properties and by discovering neighbourhood patterns revealed in the data. The result of the method would enrich data description and support comprehensive queries on sorted exit or entry points on highways and their related roads. The enrichment on road network data is important to a high successful rate of feature matching for road networks and to geospatial data integration.
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Yang, Weiping. "AUTOMATIC CONSTRUCTION OF HIERARCHICAL ROAD NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 37–44. http://dx.doi.org/10.5194/isprs-annals-iii-2-37-2016.

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This paper describes an automated method of constructing a hierarchical road network given a single dataset, without the presence of thematic attributes. The method is based on a pattern graph which maintains nodes and paths as junctions and through-traffic roads. The hierarchy is formed incrementally in a top-down fashion for highways, ramps, and major roads directly connected to ramps; and bottom-up for the rest of major and minor roads. Through reasoning and analysis, ramps are identified as unique characteristics for recognizing and assembling high speed roads. The method makes distinctions on the types of ramps by articulating their connection patterns with highways. Major and minor roads will be identified by both quantitative and qualitative analysis of spatial properties and by discovering neighbourhood patterns revealed in the data. The result of the method would enrich data description and support comprehensive queries on sorted exit or entry points on highways and their related roads. The enrichment on road network data is important to a high successful rate of feature matching for road networks and to geospatial data integration.
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Wang, Bin, Xiaoxia Pan, Yilei Li, Jinfang Sheng, Jun Long, Ben Lu, and Faiza Riaz Khawaja. "Road network link prediction model based on subgraph pattern." International Journal of Modern Physics C 31, no. 06 (April 14, 2020): 2050083. http://dx.doi.org/10.1142/s0129183120500837.

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Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.
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Liu, Yan, Siqin Wang, Xuanming Fu, and Bin Xie. "A network-constrained spatial identification of high-risk roads for hit-parked-vehicle collisions in Brisbane, Australia." Environment and Planning A: Economy and Space 51, no. 2 (October 30, 2018): 279–82. http://dx.doi.org/10.1177/0308518x18810531.

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The severe loss of human life and material damage caused by traffic accidents is a growing concern faced by many countries across the world. In Australia, despite a decline in the total number of traffic collisions since 2001, the number of hit-parked-vehicle (HPV) collisions as a special type of road accident has increased over time. Utilizing the road collisions and roadway network data in Brisbane, Australia over a 10-year period from 2001 to 2010, we generated graphics illustrating the spatial patterning of high-risk road segments for HPV crashes identified using the local indicator of network-constrained clusters (LINCS) approach. These spatial patterns vary by days of the week and times of the day. Roads with high risk for HPV collision tend to occur in high-density road networks and cluster around road intersections. The methodology applied in this work is applicable to other network-constrained point-pattern analysis.
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SUN, ZHUO, JIANFENG ZHENG, and HONGTAO HU. "FRACTAL PATTERN IN SPATIAL STRUCTURE OF URBAN ROAD NETWORKS." International Journal of Modern Physics B 26, no. 30 (October 7, 2012): 1250172. http://dx.doi.org/10.1142/s021797921250172x.

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In this paper, we investigate the fractal pattern in spatial structure of urban road networks. By introducing sub-domain, an improved box-counting algorithm is proposed to obtain the fractal pattern. Numerical experiments explore the realistic urban road network at Dalian city in China. In order to clearly show the performance of our proposed box-counting algorithm, two other measures for urban road networks, i.e., density pattern and accessibility pattern are introduced, compared and discussed.
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van Nes, Akkelies. "The Impact of the Ring Roads on the Location Pattern of Shops in Town and City Centres. A Space Syntax Approach." Sustainability 13, no. 7 (April 1, 2021): 3927. http://dx.doi.org/10.3390/su13073927.

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This contribution demonstrates how inner ring roads change the location pattern of shops in urban areas with the application of the space syntax method. A market rational behaviour persists, in that shop owners always search for an optimal location to reach as many customers as possible. If the accessibility to this optimal location is affected by changes in a city’s road and street structure, it will affect the location pattern of shops. Initially, case studies of inner ring road projects in Birmingham, Coventry, Wolverhampton, Bristol, Tampere, and Mannheim show how their realisation affect the spatial structure of the street network of these cities and the location pattern of shops. The results of the spatial integration analyses of the street and road network are discussed with reference to changes in land-use before and after the implementation of ring roads, and current space syntax theories. As the results show, how an inner ring road is connected to and the type of the street network it is imposed upon dictates the resulting location pattern of shops. Shops locate and relocate themselves along the most spatially-integrated streets. Evidence on how new road projects influence the location pattern of shops in urban centres are useful for planning sustainable city centres.
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Yang, Chao, and Qi Liu. "Road Network Pattern Classification Using GEV Distribution Parameters." International Journal of Engineering and Manufacturing 2, no. 3 (June 29, 2012): 21–29. http://dx.doi.org/10.5815/ijem.2012.03.04.

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Sreelekha, M. G., K. Krishnamurthy, and M. V. L. R. Anjaneyulu. "Interaction between Road Network Connectivity and Spatial Pattern." Procedia Technology 24 (2016): 131–39. http://dx.doi.org/10.1016/j.protcy.2016.05.019.

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Gudi, Ganga, and Dr Hanumanthappa M. "Traffic Flow Pattern in Road Network Using Clustering." Volume 5 - 2020, Issue 8 - August 5, no. 8 (August 19, 2020): 229–30. http://dx.doi.org/10.38124/ijisrt20aug206.

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Wireless communication has become important in location-based services. The enormous amount of data is extracted for useful information to solve the real world problem. Global positioning system, is used to captures the position of an object at specific time period. The scheme is finding the congested route by considering the number of vehicles in a road segment. It consists of two methods, firstly it finds the group of points based on consistency of route points and second it arranges the groups in sequence of values for each route
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Dissertations / Theses on the topic "Road network pattern"

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Rui, Yikang. "Urban Growth Modeling Based on Land-use Changes and Road Network Expansion." Doctoral thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-122182.

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A city is considered as a complex system. It consists of numerous interactivesub-systems and is affected by diverse factors including governmental landpolicies, population growth, transportation infrastructure, and market behavior.Land use and transportation systems are considered as the two most importantsubsystems determining urban form and structure in the long term. Meanwhile,urban growth is one of the most important topics in urban studies, and its maindriving forces are population growth and transportation development. Modelingand simulation are believed to be powerful tools to explore the mechanisms ofurban evolution and provide planning support in growth management. The overall objective of the thesis is to analyze and model urban growth basedon the simulation of land-use changes and the modeling of road networkexpansion. Since most previous urban growth models apply fixed transportnetworks, the evolution of road networks was particularly modeled. Besides,urban growth modeling is an interdisciplinary field, so this thesis made bigefforts to integrate knowledge and methods from other scientific and technicalareas to advance geographical information science, especially the aspects ofnetwork analysis and modeling. A multi-agent system was applied to model urban growth in Toronto whenpopulation growth is considered as being the main driving factor of urbangrowth. Agents were adopted to simulate different types of interactiveindividuals who promote urban expansion. The multi-agent model with spatiotemporalallocation criterions was shown effectiveness in simulation. Then, anurban growth model for long-term simulation was developed by integratingland-use development with procedural road network modeling. The dynamicidealized traffic flow estimated by the space syntax metric was not only used forselecting major roads, but also for calculating accessibility in land-usesimulation. The model was applied in the city centre of Stockholm andconfirmed the reciprocal influence between land use and street network duringthe long-term growth. To further study network growth modeling, a novel weighted network model,involving nonlinear growth and neighboring connections, was built from theperspective of promising complex networks. Both mathematical analysis andnumerical simulation were examined in the evolution process, and the effects ofneighboring connections were particular investigated to study the preferentialattachment mechanisms in the evolution. Since road network is a weightedplanar graph, the growth model for urban street networks was subsequentlymodeled. It succeeded in reproducing diverse patterns and each pattern wasexamined by a series of measures. The similarity between the properties of derived patterns and empirical studies implies that there is a universal growthmechanism in the evolution of urban morphology. To better understand the complicated relationship between land use and roadnetwork, centrality indices from different aspects were fully analyzed in a casestudy over Stockholm. The correlation coefficients between different land-usetypes and road network centralities suggest that various centrality indices,reflecting human activities in different ways, can capture land development andconsequently influence urban structure. The strength of this thesis lies in its interdisciplinary approaches to analyze andmodel urban growth. The integration of ‘bottom-up’ land-use simulation androad network growth model in urban growth simulation is the major contribution.The road network growth model in terms of complex network science is anothercontribution to advance spatial network modeling within the field of GIScience.The works in this thesis vary from a novel theoretical weighted network modelto the particular models of land use, urban street network and hybrid urbangrowth, and to the specific applications and statistical analysis in real cases.These models help to improve our understanding of urban growth phenomenaand urban morphological evolution through long-term simulations. Thesimulation results can further support urban planning and growth management.The study of hybrid models integrating methods and techniques frommultidisciplinary fields has attracted a lot attention and still needs constantefforts in near future.

QC 20130514

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Dilan, Askin Rasim. "Unstructured Road Recognition And Following For Mobile Robots Via Image Processing Using Anns." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612047/index.pdf.

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For an autonomous outdoor mobile robot ability to detect roads existing around is a vital capability. Unstructured roads are among the toughest challenges for a mobile robot both in terms of detection and navigation. Even though mobile robots use various sensors to interact with their environment, being a comparatively low-cost and rich source of information, potential of cameras should be fully utilized. This research aims to systematically investigate the potential use of streaming camera images in detecting unstructured roads. The investigation focused on the use of methods employing Artificial Neural Networks (ANNs). An exhaustive test process is followed where different kernel sizes and feature vectors are varied systematically where trainings are carried out via backpropagation in a feed-forward ANN. The thesis also claims a contribution in the creation of test data where truth images are created almost in realtime by making use of the dexterity of human hands. Various road profiles v ranging from human-made unstructured roads to trails are investigated. Output of ANNs indicating road regions is justified against the vanishing point computed in the scene and a heading vector is computed that is to keep the robot on the road. As a result, it is shown that, even though a robot cannot fully rely on camera images for heading computation as proposed, use of image based heading computation can provide a useful assistance to other sensors present on a mobile robot.
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Ekinci, Murat. "Computer vision applied to the navigation of an autonomous road vehicle in complex road networks." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361132.

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Han, Binh Thi. "Mining mobile object trajectories: frameworks and algorithms." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52968.

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The proliferation of mobile devices and advances in geo-positioning technologies has fueled the growth of location-based applications, systems and services. Many location-based applications have now gained high popularity and permeated the daily activities of mobile users. This has led to a huge amount of geo-location data generated on a daily basis, which draws significant interests in analyzing and mining ubiquitous location data, especially trajectories of mobile objects moving in road networks (MO trajectories). Mobile trajectories are complex spatio-temporal sequences of location points with varying sample sizes and varying lengths. Mining interesting patterns from large collection of complex MO trajectories presents interesting challenges and opportunities which can reveal valuable insights to the studies of human mobility in many perspectives. This dissertation research contributes original ideas and innovative techniques for mining complex trajectories from whole trajectories, from subtrajectories of significant characteristics, and from semantic location sequences within large-scale datasets of MO trajectories. Concretely, the first unique contribution of this dissertation is the development of NEAT, a three-phase road-network aware trajectory clustering framework to organize MO subtrajectories into spatial clusters representing highly dense and highly continuous traffic flows in a road network. Compared with existing trajectory clustering approaches, NEAT yields highly accurate clustering results and runs orders of magnitude faster by smartly utilizing traffic locality with respect to physical constraints of the road network, traffic flows among consecutive road segments and flow-based density of mobile traffic as well as road network based distances. The second original contribution of this dissertation is the design and development of TraceMob, a methodical and high performance framework for clustering whole trajectories of mobile objects. To our best knowledge, this is the first whole trajectory clustering system for MO trajectories in road networks. The core idea of TraceMob is to develop a road-network aware transformation algorithm that can map complex trajectories of varying lengths from a road network space into a multidimensional data space while preserving the relative distances between complex trajectories in the transformed metric space. The third novel contribution is the design and implementation of a fast and effective trajectory pattern mining algorithm TrajPod. TrajPod can extract the complete set of frequent trajectory patterns from large-scale trajectory datasets by utilizing space-efficient data structures and locality-aware spatial and temporal correlations for computational efficiency. A comprehensive performance study shows that TrajPod outperforms existing sequential pattern mining algorithms by an order of magnitude.
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Loraamm, Rebecca Whitehead. "Road-based Landscape Metrics for Quantifying Habitat Fragmentation." Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3214.

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Landscape metrics measure the composition and configuration of habitats within landscapes; often the goal is to measure fragmentation. While a variety of existing metrics characterize the connectivity and contiguity of habitat patches, most do not explicitly consider the fragmenting effects of roads in their formulations. This research develops a set of new landscape metrics that explicitly quantify how roads disconnect and break apart habitat patches. This research introduces the following four metrics to consider the fragmenting effects of transportation networks: (1) Number of Connected Patches, a measure of connectivity; (2) Euclidean Nearest Neighbor-Roads, a measure of proximity; (3) Road Density, a measure of dispersion, and (4) Distance to Roads, a measure of division. Each of these formulations explicitly considers the presence of roadways. The metrics are applicable at three spatial scales: patch, class, and landscape. Number of Connected Patches (NCP) provides a new roadway-sensitive measure of patch connectivity by computing the number of patches of identical cover type in a landscape that can be traversed on the shortest straight line distance between them without crossing a road. Euclidean Nearest Neighbor-Roads (ENNR) calculates the distance to the Euclidean nearest neighbor of a patch of the same cover type that is not separated by a roadway. Road Density (RD) leverages the ratio of total roadway network length intersecting a patch, class, or landscape versus respective total unit area. Distance to Roads (DR) provides a new measure of division by taking the shortest Euclidean distance in meters of any patch to the nearest roadway. The performance of the new metrics is evaluated using simulated landscape data with different transportation network structures and habitat patch configurations. This is accomplished by comparing output from the road-based metrics to existing metrics that quantify habitat density, isolation, dispersion, and division. The results of the study demonstrate that the new road-based landscape metrics provide an improved means of quantifying habitat fragmentation caused by transportation networks. This is especially evident as simulated transit network increases for each landscape; response of new metrics to increased road presence is linear and as expected given metric design. These metrics have successfully captured notable patch, class, and landscape level characteristics and their associated responses which are not available with treatment by conventional measures of landscape fragmentation.
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Naylor, Benjamin Walden. "Reassembling the Iberians : rain, road, coins, crops and settlement in central Hispania Citerior, 206-27 B.C." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/11347.

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This thesis investigates Iberian communities in central Hispania Citerior during the Roman Republic. I demonstrate the usefulness of an actor-network approach for understanding a topic characterised by scarce archaeological datasets. This approach is not intended to create a new narrative for Roman Provincial Studies but instead allows us to ask new questions: what was at stake for these communities? What was of interest to the Iberians? How did things happen? Iberians lived primarily in small, often fortified settlements in elevated locations, although some larger settlements are known and during the Republic many sites were abandoned for new locations on flatter ground. I find that throughout the period settlements were often clustered, creating communities distributed in small groups of sites. These Iberian groups grew versatile staple crops in a variety of locations but may have tailored additional crops to regional environmental conditions. I consider the potential for collaboration in the autumn ploughing and conclude that any such collaboration must have relied on dense and wide relationships given changing patterns of variability in rainfall. I show differences within coin circulation that suggest Iberian coins were part of distinct sets of relationships. I also test the ability of carts to pass over various long-distances routes and find that some coins were bound up in the same assemblages as cart transport. The thesis positions the interface between all these different assemblages as crucial to further work on these communities.
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Mon-Ma, Marly Mitiko. "Análise da importância das variáveis intervenientes nos acidentes de trânsito em interseções urbanas utilizando redes neurais artificiais." Universidade Federal de São Carlos, 2005. https://repositorio.ufscar.br/handle/ufscar/4403.

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Made available in DSpace on 2016-06-02T20:01:03Z (GMT). No. of bitstreams: 1 DissMMM.pdf: 1370681 bytes, checksum: 4891da17b25caf7951985f7209d9c825 (MD5) Previous issue date: 2005-10-06
The technological development has generated great amount of potential data bases in order to supply information for several aspects related to road safety. However, the transformation of these great amount of data in useful information for the technicians, public managers and the population in general, requests the modeling and the treatment of these data using some analysis tools that allow a visualization of the results in form easily understandable. This work presents a new methodology of traffic accidents analysis based in the artificial neural network (ANN). ANN can be very useful for organizations, public or particular, mainly to those that propose to understand the phenomena of the traffic in order to looking for solutions integrated to several areas such as education, engineering and fiscalization. This research had as general objective to identify the patterns of traffic accidents that happened at urban intersections. The data of accidents that happened in the period from 2000 to 2003, in the city of São Carlos were used for the case study, in order to subsidize the elaboration and the evaluation of public policies of traffic accidents reduction and specially the reduction of accident severity. The study explores the assumption that different accident types are related to different patterns. The patterns obtained by ANN showed that there are significant differences in the factors that can affect the different types of accidents. The knowledge of the patterns of each accident type is essential to develop actions corrective or preventive road safety's improvement in order to avoid undesirable effects when these actions are implemented. However, the comparison between the patterns of the different types of accidents was difficult due to the heterogeneity of the situations and the different elements that compose the road environment that can affect the occurrence of the accident.
O desenvolvimento tecnológico tem gerado grandes bases de dados com potencial para fornecer informações sobre diversos aspectos relacionados com a segurança viária. No entanto, a conversão de um grande volume de dados em informações úteis para os técnicos, gestores públicos e a população em geral, requer a modelagem e o tratamento destes dados utilizando ferramentas de análise que permitam uma visualização dos resultados de forma facilmente compreensível. Este trabalho apresenta uma nova metodologia para análise de acidentes de trânsito fundamentada na rede neural artificial (RNA). A RNA pode ser de grande utilidade para organizações públicas e privadas, principalmente para aquelas que se propõem compreender os fenômenos do trânsito a fim de buscar soluções integradas em diversas áreas tais como educação, engenharia e fiscalização. A pesquisa teve como objetivo geral identificar os padrões de acidentes de trânsito que ocorreram nas interseções urbanas. Os dados de acidentes que ocorreram no período de 2000 a 2003, na cidade de São Carlos foram utilizados para o estudo de caso, visando fornecer subsídios para a elaboração e a avaliação de políticas públicas voltadas para redução do número de acidentes de trânsito e essencialmente na redução global da severidade. O estudo explora a suposição de que diferentes tipos de acidente estão relacionados com padrões distintos. Os padrões obtidos através da RNA mostram que há divergências significativas nos fatores que podem influenciar os diferentes tipos de acidentes. Conhecer padrões de cada tipo de acidente se faz necessária para que as medidas corretivas ou preventivas voltadas para a melhoria da segurança viária não resultem em efeitos indesejados quando são implementadas, no entanto comparações entre padrões de diferentes tipos de acidentes mostraram-se particularmente difíceis devido à heterogeneidade das situações e dos diferentes elementos que compõem o ambiente viário e que podem influenciar na ocorrência do acidente.
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Yang, Chien-Ying, and 楊千瑩. "An Exploratory Study on Pattern characteristics of the main road of network between Cities in Taiwan." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/46152438575145724508.

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碩士
國立成功大學
都市計劃學系碩博士班
90
First, based on the real-map analysis of main highway networks between cities, eight major network patterns are found. The network patterns between cities of Taiwan are also generalized on the basis of urban population. Secondly, across-analysis between network patterns and factors including urban population. Highway classification and geographic environment is done. The relationship between the urban network patterns and above factors are then analyzed. Finally, we focus on the network patterns of connecting roads between major cities and according to time and space dimensions we try to do a chronological analysis. This analysis is done no matter if it is an entire road network plan or only a regional road development. Thus, we can generalize and compare the characteristics and differences of these road patterns through time and space.
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Wen, Tsun-Jui, and 温存睿. "GPS Data Based Speed Pattern Estimation and Route Guidance in City Road Networks." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/34323831443968288990.

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碩士
臺灣大學
資訊管理學研究所
99
Traffic congestion is an important problem in city. It could lead a significant waste of money and time. In recent years, cars equipped with GPS devices become widespread and the location information of those cars could be very useful to estimate traffic condition in the complex city road network. According to the accurate traffic condition estimation, we can provide appropriate route guidance to road drivers and they can avoid the congestion. In this thesis, we use the GPS coordinates of cars driving on the city road network to estimate the traffic condition of road segments. We propose a speed pattern model to describe traffic condition as the travel speed pattern. And we propose a classification-based route guidance model by learning the historic traffic data using machine learning technique. The route guidance model could provide route guidance to drivers according to current traffic condition and how traffic condition would change by the experience learned from historic traffic data.
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Chiu, Shu-Mei, and 邱淑美. "Analysis and Estimation of the Effect on Landscape Patternby Rural Road Network." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/yptrpw.

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碩士
國立臺灣大學
園藝學研究所
92
The construction of road and the utilization of land are interrelated with each other. To change the land utilization will have influence both on the road network and landscape which result will shape the space of landscape pattern. The road network is an important element of landscape structure, which change will have strong impact on the procedure operation of landscape pattern. The construction of road will result the split of land which is the major reason for the landscape fragmentation. The rural area is between natural and artificial environments which is sensitive to the environment. The fragementation, interruption and destruction of landscape pattern are resulted from the construction of road which has a negative impact on the national land reservation and security. The concept of systematic planning for a coordinated nature and landscape procedure of rural road network is always ignored by the public, which contribute to the high density and quick growth of rural road hierarchy. The study attempts to integrate rural landscape pattern by quality and quantity method analysis and chooses FuLi Town, ChihSang Town and KwanShan Town of HuaLien/TaiTung Longitudinal Valley, which are the major area of rice production, served as the study sample of actual experiment. Furthermore, the study will adopt new edition of year 90 Taiwan area photos, completed by Agricultureal and Forestry Aerial Survey Institute（A.F.A.S.I.）as basic drawings and utilize GIS as an analytic tool to erect the pattern of space analysis and evaluate the impact on rural landscape fragmentation from road network, which conduct route analysis of network optimization. In order to conduct network integration, the study applies the concepts of traffic calmed rural area (TCRA) and landscape connectivity to the strategy and principle of network adjustment, and raise the layout schemes of last-forever rural road network and related counterpart means to reduce the negative impact on the rural and landscape fragmentation from the road network. From the result of experimental study, the rural landscape structure of three sample areas have a very high similarity. The landscape of FuLi Town is dominated by huge type of forest pattern. With rich and even resources, ChihSang Town’s landscape is dominated by huge type of forest pattern slightly. However, the landscape of KwanShan Town is dominated by several landscape patterns. After the adjustment of road network, the level of landscape fragmentation is lower down for three sample areas. Comparing with the level before the adjustment, ChihSang Town’s landscape has the strongest influence from the road construction. The landscape of KwanShan Town is not only influenced by the road splitting, but also dominated by the land utilization. The landscape of FuLi Town is the most influenced sample area by specific landscape type with strong domination among three chosen sample areas.
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Books on the topic "Road network pattern"

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Cushman, Samuel A., and Tzeidle N. Wasserman. Quantifying loss and degradation of former American marten habitat due to the impacts of forestry operations and associated road networks in northern Idaho, USA. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198759805.003.0012.

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American marten are associated with extensive and unfragmented late seral forest habitats, and are often considered to be particularly vulnerable to habitat loss and fragmentation. This chapter evaluates the impact of road building and timber harvest on habitat suitability for marten in northern Idaho, USA, using an empirically derived, multi-scale habitat suitability model, reconstructing key predictor variables (elevation, forest type, road density, canopy cover, landscape fragmentation and the extensiveness of late seral forest in the landscape) as they appear to have existed prior to harvest, and applying the model to both current and pre-harvest conditions. Calculating changes in the extent and pattern of habitat in the landscape indicate that timber harvest and road construction together reduced marten habitat quality considerably across the study area, which is likely responsible for current patterns of reduced detection rates and lower genetic diversity in areas that have experienced the largest amounts of habitat loss.
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Poehler, Eric E. The Traffic Systems of Pompeii. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190614676.001.0001.

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The Traffic Systems of Pompeii is the first sustained examination of the evidence for a regulated circulation of wheeled traffic in the ancient world. The setting to this system is the six-hundred-year evolution of Pompeii’s street network, the focus of which telescopes from the city’s urban grid to the shape of the streets, the treatment of their surfaces, and finally the individual elements of construction—the curbstones, stepping stones, and guard stones—where the evidence for traffic was inscribed. Although ruts are the most evocative evidence of ancient traffic, it is the wearing patterns on the vertical faces of street features that permit the determination of the directions that ancient carts were traveling and undergird the argument for their systematic regulation. Distilled from over five hundred locations recording multiple categories of evidence, all wholly new to archaeology and unique to this research, this book reveals the basic rules of the road and at the same time opens larger historical questions. What does the existence of a traffic system mean for our understanding of ancient urbanism? What other social forces are uncovered in the search for it? To explore these questions, the traffic system at Pompeii is set in its broader contexts as one infrastructural and administrative artifact of the Roman empire, an epiphenomenon of a deeply urban culture.
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Book chapters on the topic "Road network pattern"

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Sani, Zamani Md, Liang Jin Chuan, Tarmizi Ahmad Izzudin, Hadhrami Abd Ghani, and Aerun Martin. "Urban Road Marker Classification Using Histogram of Oriented Gradient and Local Binary Pattern with Artificial Neural Network." In Proceedings of International Conference on Emerging Technologies and Intelligent Systems, 126–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82616-1_12.

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Heinzle, Frauke, Karl-Heinrich Anders, and Monika Sester. "Pattern Recognition in Road Networks on the Example of Circular Road Detection." In Geographic Information Science, 153–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11863939_11.

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Jie, Zequn, Wen Feng Lu, and Eng Hock Francis Tay. "Accurate On-Road Vehicle Detection with Deep Fully Convolutional Networks." In Machine Learning and Data Mining in Pattern Recognition, 643–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41920-6_50.

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Kress, Viktor, Stefan Zernetsch, Konrad Doll, and Bernhard Sick. "Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks." In Pattern Recognition. ICPR International Workshops and Challenges, 57–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68763-2_5.

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Gao, Hepeng, Yongjian Yang, Liping Huang, Yiqi Wang, Bing Jia, Funing Yang, and Zhuo Zhu. "Trajectory Data-Driven Pattern Recognition of Congestion Propagation in Road Networks." In Algorithms and Architectures for Parallel Processing, 199–211. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05054-2_15.

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Qiu, Ming, and Dechang Pi. "Mining Frequent Trajectory Patterns in Road Network Based on Similar Trajectory." In Lecture Notes in Computer Science, 46–57. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46257-8_6.

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Barauskas, Andrius, Agnė Brilingaitė, Linas Bukauskas, Vaida Čeikutė, Alminas Čivilis, and Simonas Šaltenis. "Semi-synthetic Data and Testbed for Long-Distance E-Vehicle Routing." In New Trends in Database and Information Systems, 61–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85082-1_6.

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AbstractElectric and autonomous mobility will increasingly rely on advanced route planning algorithms. Robust testing of these algorithms is dependent on the availability of large realistic data sets. Such data sets should capture realistic time-varying traffic patterns and corresponding travel-time and energy-use predictions. Ideally, time-varying availability of charging infrastructure and vehicle-specific charging-power curves should be included in the data to support advanced planning.We contribute with a modular testbed architecture including a semi-synthetic data generator that uses a state-of-the-art traffic simulator, real traffic distribution patterns, EV-specific data, and elevation data to generate time-dependent travel-time and energy-use weights in a road-network graph. The experimental study demonstrates that the testbed can reproduce travel-time and energy-use patterns for long-distance trips similar to commercially available services.
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Thianniwet, Thammasak, Satidchoke Phosaard, and Wasan Pattara-Atikom. "Classification of Road Traffic Congestion Levels from Vehicle’s Moving Patterns: A Comparison Between Artificial Neural Network and Decision Tree Algorithm." In Lecture Notes in Electrical Engineering, 261–71. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8776-8_23.

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Fuller, Michael S., and Peter D. Roffers. "Erosion due to a century of road construction and maintenance at Mount Diablo State Park, California." In Regional Geology of Mount Diablo, California: Its Tectonic Evolution on the North America Plate Boundary. Geological Society of America, 2021. http://dx.doi.org/10.1130/2021.1217(07).

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ABSTRACT Mount Diablo State Park exemplifies many other conservation areas where managers balance the dual missions of protecting natural resources while providing public access. Roads and trails that crisscross the park are etched into the geomorphic surface, capturing and redirecting storm runoff, and presenting both a challenge for soil conservation and a consequence of construction and maintenance. We used field mapping, remote sensing, and modeling to assess erosion along the roads and trails in Mount Diablo State Park, which encompasses the headwaters of several urbanized watersheds. The field mapping in 2011 determined that 56% of the assessed roads and trails required either repair or reconstruction to control erosion and that ~67% of the culverts in the park required either repair or replacement. Aerial photography and modeling showed that other erosion (unrelated to roads or trails) preferentially occurred during wet periods, in specific lithologies, and on convergent slopes. Although lithology and climate drive slope-forming geomorphic processes, we found that the road and trail system (1) expanded the stream network with a capillary-like system of rills, (2) catalyzed prolonged erosion, and (3) altered the timing and pattern of sediment yield. In addition to water-driven erosion during wet periods, road and trail surfaces were subject to mechanical and wind erosion during dry periods. Spatially, dry erosion and runoff both conformed with and crossed topographic gradients by following the road and trail network. Road- and trail-induced erosion occurred across a wider range of rock properties and slope geometries than is typical for other erosion. Hence, the roads and trails have expanded the spatial and temporal boundary conditions over which geomorphic processes operate and, due to continual soil disturbance, have accelerated erosion rates. Although road density is a commonly used metric to rank road-related impacts at watershed scales, it misses both spatial variability and the opportunity to identify specific road and trail segments for remediation. We developed a spatially explicit scoring scheme based on actual erosion and the potential for sedimentation of discrete waterbodies. The data were incorporated into the park’s road and trail management plan in 2016.
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Patil, Vilas K., and P. P. Nagarale. "Prediction of L10 and Leq Noise Levels Due to Vehicular Traffic in Urban Area Using ANN and Adaptive Neuro-Fuzzy Interface System (ANFIS) Approach." In Research Anthology on Artificial Neural Network Applications, 597–611. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch027.

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Recently in urban areas, road traffic noise is one of the primary sources of noise pollution. Variation in noise level is impacted by the synthesis of traffic and the percentage of heavy vehicles. Presentation to high noise levels may cause serious impact on the health of an individual or community residing near the roadside. Thus, predicting the vehicular traffic noise level is important. The present study aims at the formulation of regression, an artificial neural network (ANN) and an adaptive neuro-fuzzy interface system (ANFIS) model using the data of observed noise levels, traffic volume, and average speed of vehicles for the prediction of L10 and Leq. Measured noise levels are compared to the noise levels predicted by the experimental model. It is observed that the ANFIS approach is more superior when compared to output given by regression and an ANN model. Also, there exists a positive correlation between measured and predicted noise levels. The proposed ANFIS model can be utilized as a tool for traffic direction and planning of new roads in zones of similar land use pattern.
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Conference papers on the topic "Road network pattern"

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Cheng, Pengxue, and Maolin Hu. "Urban road network extraction from SAR image." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Yongji Wang, Jun Li, Bangjun Lei, and Jingyu Yang. SPIE, 2007. http://dx.doi.org/10.1117/12.742465.

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Hu, Guoqiang, Ning Duan, and Jun Zhu. "Lightweight road network learning for efficient trajectory pattern mining." In 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). IEEE, 2016. http://dx.doi.org/10.1109/soli.2016.7551666.

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Liang, Justin, Namdar Homayounfar, Wei-Chiu Ma, Shenlong Wang, and Raquel Urtasun. "Convolutional Recurrent Network for Road Boundary Extraction." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00974.

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Sirmacek, Beril, and Cem Unsalan. "Road Network Extraction Using Edge Detection and Spatial Voting." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.762.

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Sun, Yiwen, Kun Fu, Zheng Wang, Changshui Zhang, and Jieping Ye. "Road Network Metric Learning for Estimated Time of Arrival." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412145.

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Wegner, Jan D., Javier A. Montoya-Zegarra, and Konrad Schindler. "A Higher-Order CRF Model for Road Network Extraction." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.222.

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Kinattukara, Tejy, and Brijesh Verma. "Clustering based neural network approach for classification of road images." In 2013 International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2013. http://dx.doi.org/10.1109/socpar.2013.7054121.

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Buslaev, Alexander, Selim Seferbekov, Vladimir Iglovikov, and Alexey Shvets. "Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2018. http://dx.doi.org/10.1109/cvprw.2018.00035.

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Xu, Xianrui, and Zhongren Peng. "The K-function analysis of space-time point pattern on road network." In 2011 19th International Conference on Geoinformatics. IEEE, 2011. http://dx.doi.org/10.1109/geoinformatics.2011.5981103.

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Courtrai, Luc, and Sebastien Lefevre. "Road network extraction from remote sensing using region-based mathematical morphology." In 2014 8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS). IEEE, 2014. http://dx.doi.org/10.1109/prrs.2014.6914283.

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Reports on the topic "Road network pattern"

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Albrecht, Jochen, Andreas Petutschnig, Laxmi Ramasubramanian, Bernd Resch, and Aleisha Wright. Comparing Twitter and LODES Data for Detecting Commuter Mobility Patterns. Mineta Transportation Institute, May 2021. http://dx.doi.org/10.31979/mti.2021.2037.

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Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to extrapolate to overall traffic demand is indeed justified. This means that expensive and rarely comprehensive surveys are now only needed to capture trip purposes. Regardless of trip purpose (e.g., shopping, regular recreational activities, dropping kids at school), the LODES data is an excellent predictor of overall road segment loads.
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