2,223 research outputs found

    Developing a Pedestrian Route Network Service (PRNS)

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    Route network service is becoming increasingly popular. However, although there are significant amount of route network services there are still limitations especially to pedestrian network services. Pedestrians daily make decision about their navigation choices. Developing a pedestrian route network service (PRNS) involves several factors. During this study the analysis of several routing network services have demonstrated that the geographical data is one of the most important factors in order to develop an own PRNS. Considering the idea of estimation two different datasets for the PRNS were evaluated OpenStreetMap (OSM) and Swedish national road database (NVDB). The use of the OSM dataset for developing the PRNS was made after the comparison between both the dataset. OSM has shown more advantages in terms of completeness of route for pedestrian navigation than NVDB. The OSM dataset was created and stored in the PostGIS database. The implementation of own pedestrian network service is intended to facilitate the developments of new PRNS and analysis and comparison of others existing PRNS. The calculation and collection of the routes to be displayed for the user are performed by extended tools within PostGIS such as pgRouting and PostgreSQL respectively. The dataset’s network topology is related to the distance and determination of route choice by the pedestrian. Thus, Geographical Information System (GIS) is also one fundamental factor used in this study to evaluate and create results. The application was implemented in the city of Lund. One of the limitations developing the PRNS is the lack of documentation for new functions which are released by pgRouting developers. Although OSM provides an essential network for developing the PRNS, some closed residential areas, parks, and open areas are not include on the network limiting the PRNS application. In conclusion the PRNS is a useful application in order to assist pedestrians on their wayfinding in the city of Lund. It is also intended to help further development of new PRNS such as mobile PRNS applications. However, the PRNS must be improved and the dataset network requires updating and expansion for successful operations of the PRNS applications.NavigeringstjĂ€nster blir allt mer populĂ€ra, men Ă€ven om det finns en betydande mĂ€ngd tjĂ€nster, finns det fortfarande begrĂ€nsningar, speciellt för fotgĂ€ngare. FotgĂ€ngare gör dagligen flera val om hur de ska hitta den bĂ€sta vĂ€gen i sin nĂ€rmiljö. Att utveckla en navigeringstjĂ€nst för gĂ„ende (PRNS) involverar flera viktiga faktorer. I denna studie analyseras flera befintliga nĂ€ttjĂ€nster och studien visar att geografiska data Ă€r en av de viktigaste faktorerna för att utveckla egna PRNS. TvĂ„ olika datamĂ€ngder för PRNS utvĂ€rderades: OpenStreetMap (OSM) och svenska nationella vĂ€gdatabasen (NVDB). Efter en första utvĂ€rdering av de tvĂ„ datamĂ€ngderna valdes OSM som visade sig ha flera fördelar för en fotgĂ€ngares navigering. Implementeringen av en egen navigeringstjĂ€nst för gĂ„ende Ă€r avsedd att underlĂ€tta utvecklingen av nya PRNS och analysering och jĂ€mförelsen av andra befintliga PRNS. pqRouting och PostgreSQL berĂ€knar och samlar in de rutter som ska anvĂ€ndas, med hjĂ€lp av datamĂ€ngden som finns i databasen PostGIS. Topologin för nĂ€tverket i datamĂ€ngden Ă€r relaterat till avstĂ„nden och fotgĂ€ngaren bestĂ€mmer vĂ€gvalet. Geografiska informationssystem (GIS) Ă€r ocksĂ„ en grundlĂ€ggande faktor som anvĂ€nds i den hĂ€r studien för att analysera resultatet. PRNS implementerades i staden Lund och syftet Ă€r att underlĂ€tta för fotgĂ€ngare att navigera i staden. En av begrĂ€nsningarna vid utvecklandet av PRNS Ă€r bristen pĂ„ dokumentation av vissa nya funktionaliteter i pgRouting. Även om OSM tillhandahĂ„ller ett grundlĂ€ggande nĂ€tverk av vĂ€gar för utvecklingen av PRNS, sĂ„ saknas information om vissa bostadsomrĂ„den, parker och allmĂ€nna utrymmen, vilket begrĂ€nsar PRNS. Sammanfattningsvis sĂ„ Ă€r PRNS ett anvĂ€ndbart program för att hjĂ€lpa fotgĂ€ngare att vĂ€lja vĂ€g i staden Lund. Vidare utveckling av PRNS kan exempelvis vara en mobilapplikation. DĂ„ mĂ„ste dock PRNS förbĂ€ttras och datamĂ€ngden krĂ€ver uppdatering och utveckling för att bli framgĂ„ngsrik.Internet based applications for finding shortest ways to travel are becoming increasingly popular. However, although there are significant amount of these applications there are still limitations especially for pedestrian. Developing an internet based application service for pedestrian involves several steps. During this study the analysis of several internet based applications service for pedestrian have demonstrated that the geographical data is one of the most important components in order to develop the PRNS. Two different dataset were evaluated OpenStreetMap (OSM) and Swedish national road database (NVDB). The use of the OSM dataset for developing the PRNS was made after the comparison between both the dataset. OSM has shown more advantages in terms of completeness of roads network and ways for pedestrian than NVDB. The OSM dataset was created and stored in a database called PostGIS. The calculation of distances and collection of the shortest ways to be displayed for the user are performed by extended tools and programs within PostGIS such as pgRouting and PostgreSQL respectively. The distance and determination of the shortest ways to walk is based on the structure of the dataset and the user choice. Thus, application such as Geographical Information System (GIS) is also one fundamental component used in this study, for example, to evaluate and create maps for visual analysis. One of the limitations developing the PRNS is the lack of documentation for new functions which are released by developers. Some closed residential areas, parks, and open areas are not included on the network limiting the PRNS application. In conclusion the PRNS is a useful application in order to assist pedestrians on their wayfinding in the city of Lund. It is also intended to help further development of new PRNS such as mobile PRNS applications. However, the PRNS must be improved and the dataset network requires updating and expansion for successful operations of the PRNS applications

    A case study of route solving for oversized transport : the use of GIS functionalities in transport of transformers, as part of maintaining a reliable power infrastructure

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    Power supply is a social necessity, and will so continue to be in the near future. Therefore, safe and steady deliverance of power supply is a fundamental duty of the societies and the preparedness of the vulnerability of the infrastructures is highly prioritized. The power supply comprises every component needed for transmission of electrical power from generation to consumption, power transformers are key components of this network, transforming electrical power to customized levels. The aim of this master thesis was to present methods on how to improve the establishment of power transformers. More specifically, the scope of the aim was to improve the timeframe for the exchange of transformers by simplifying and improving the aspect of route planning, and thereby to generate improved transformer preparedness. This was done using Geographical Information Systems, involving the building of a multimodal network dataset and performing route calculations. The location of the study was set to southern Norway and the distance between the connector stations Sylling and StĂžlen. The analysis was based on information about the sea ways, landfalls, electronic road network, height, road block, weight, speed and distance. A method for effective route planning was developed and the distance Sylling-StĂžlen was used in a case study, where modeled and actual transport routines were compared. The results show a difference between the actual and the modeled routes, both on duration and path, mainly due to a low focus on bridges and to inaccurate data. The overall difference in time was minor, the modeled route diverging only one hour from the actual, the difference on specific stretches were however greater, either witnessing of potential of faster transport or data which has not been adapted close enough to the actual situation.StrĂžmforsyning er et nĂždvendig gode for dagens samfunn og vil vĂŠre det i fremtiden. Å sikre en trygg og stabil leveranse av strĂžm er en grunnleggende oppgave for samfunnet, beredskapen av den kritiske infrastruktur mĂ„ derfor vĂŠre hĂžyt prioritert. StrĂžmforsyningen omfatter alle komponenter som er nĂždvendige for overfĂžring av elektrisitet fra produsent til forbruker. Transformatorer er sentrale komponenter i strĂžmnettet og har som oppgave Ă„ transformere strĂžmmen til et brukertilpasset nivĂ„. MĂ„let for denne masteroppgaven har vĂŠrt Ă„ utvikle metoder som forbedrer prosessen rundt transformator bytte. NĂŠrmere bestemt sĂ„ har oppgaven sett pĂ„ om det er mulig Ă„ redusere tiden pĂ„ et transformatorbytte, ved Ă„ forenkle og utvikle ruteplanlegging. Det er et hĂ„p Ă„ generere bedre beredskap av transformatorer pĂ„ denne mĂ„ten. I oppgaven har det vĂŠrt bygget multimodale nettverks datasett og utfĂžrt rute beregninger i Geografiske informasjonssystemer. StudieomrĂ„det har omfattet SĂžr-Norge, nĂŠrmere bestemt strekningen mellom Sylling og StĂžlen. Analysen har vĂŠrt basert pĂ„ data om farleder, landingsplasser, elektronisk vegnett, hĂžyde-, vekt- og farts restriksjoner, veg sperringer og ikke minst avstander. Det ble i lĂžpet av prosjektet utviklet en metode for effektiv ruteplanlegging hvor strekningen Sylling - StĂžlen ble brukt som utgangspunkt. Resultatene fra den modellerte og den faktiske ruten ble til slutt sammenliknet. Resultatene viser en differanse mellom den faktiske og den modellerte ruten, bĂ„de i forhold til tidsbruk og veivalg, fĂžrst og fremst grunnet et for lavt fokus pĂ„ bro-attributtene og unĂžyaktige data. Den overordnede forskjellen i tid var liten, faktisk bare en time, differansen mellom mer spesifikke strekninger derimot var stĂžrre. Resultatet beskriver at det enten er mulig Ă„ redusere den virkelige tidsbruken, eller at dataene ikke har vĂŠrt tilpasset virkeligheten godt nok

    Impact of Structural Damage on Network Accessibility Following a Disaster: the Case of the Seismically Damaged Port Au Prince Urban Road Network

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    The catastrophic seismic event that struck Haiti in January 2010 led to an unprecedented effort in collecting and providing geographical information in support of the humanitarian aid. Although most of the compiled datasets and generated maps try to provide specific and detailed information on the location of damage and road interruptions, little or no information was available in terms of accessibility of the urban space. Here we try to offer an alternative method in defining the urban aftermath damage, coupling graph theory and GIS-based spatial analysis to assess how the urban space accessibility decreases when the road network is damaged. We believe there could be important lessons to be learnt from this exercise in the event of the physical failure of critical elements of European infrastructure.JRC.DG.G.5-European laboratory for structural assessmen

    Data organization for routing on the multi-modal public transportation system: a GIS-T prototype of Hong Kong Island.

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    Yu Hongbo.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 130-138).Abstracts in English and Chinese.ABSTRACT IN ENGLISH --- p.i-iiABSTRACT IN CHINESE --- p.iiiACKNOWLEDGEMENTS --- p.iv-vTABLE OF CONTENTS --- p.vi-viiiLIST OF TABLES --- p.ixLIST OF FIGURES --- p.x-xiChapter CHAPTER I --- INTRODUCTIONChapter 1.1 --- Problem Statement --- p.1Chapter 1.2 --- Research Purpose --- p.5Chapter 1.3 --- Significance --- p.7Chapter 1.4 --- Methodology --- p.8Chapter 1.5 --- Outline of the Thesis --- p.9Chapter CHAPTER II --- LITERATURE REVIEWChapter 2.1 --- Introduction --- p.12Chapter 2.2 --- Origin of GIS --- p.12Chapter 2.3 --- Development of GIS-T --- p.15Chapter 2.4 --- Capabilities of GIS-T --- p.18Chapter 2.5 --- Structure of a GIS-T --- p.19Chapter 2.5.1 --- Data Models for GIS-T --- p.19Chapter 2.5.2 --- Relational DBMS and Dueker-Butler's Data Model for Transportation --- p.22Chapter 2.5.3 --- Objected-oriented Approach --- p.25Chapter 2.6 --- Main Techniques of GIS-T --- p.26Chapter 2.6.1 --- Linear Location Reference System --- p.26Chapter 2.6.2 --- Dynamic Segmentation --- p.27Chapter 2.6.3 --- Planar and Non-planar Networks --- p.28Chapter 2.6.4 --- Turn-table --- p.28Chapter 2.7 --- Algorithms for Finding Shortest Paths on a Network --- p.29Chapter 2.7.1 --- Overview of Routing Algorithms --- p.29Chapter 2.7.2 --- Dijkstra's Algorithm --- p.31Chapter 2.7.3 --- Routing Models for the Multi-modal Network --- p.32Chapter 2.8 --- Recent Researches on GIS Data Models for the Multi-modal Transportation System --- p.33Chapter 2.9 --- Main Software Packages for GIS-T --- p.36Chapter 2.10 --- Summary --- p.37Chapter CHAPTER III --- MODELING THE MULTI-MODAL PUBLIC TRANSPORTATION SYSTEMChapter 3.1 --- Introduction --- p.40Chapter 3.2 --- Elaborated Stages and Methods for GIS Modeling --- p.40Chapter 3.3 --- Application Domain: The Multi-modal Public Transportation System --- p.43Chapter 3.3.1 --- Definition of a Multi-modal Public Transportation System --- p.43Chapter 3.3.2 --- Descriptions of the Multi-modal Public transportation System --- p.44Chapter 3.3.3 --- Objective of the Modeling Work --- p.46Chapter 3.4 --- A Layer-cake Based Application Domain Model for the Multi- modal Public Transportation System --- p.46Chapter 3.5 --- A Conceptual Model for the Multi-modal Public Transportation System --- p.49Chapter 3.6 --- Logical and Physical Implementation of the Data Model for the Multi-modal Public Transportation System --- p.54Chapter 3.7 --- Criteria for Routing on the Multi-modal Public Transportation System --- p.57Chapter 3.7.1 --- Least-time Routing --- p.58Chapter 3.7.2 --- Least-fare Routing --- p.60Chapter 3.7.3 --- Least-transfer Routing --- p.60Chapter 3.8 --- Summary --- p.61Chapter CHAPTER IV --- DATA PREPARATION FOR THE STUDY AREAChapter 4.1 --- Introduction --- p.53Chapter 4.2 --- The Study Area: Hong Kong Island --- p.63Chapter 4.2.1 --- General Information of the Transportation System on Hong Kong Island --- p.63Chapter 4.2.2 --- Reasons for Choosing Hong Kong Island as the Study Area --- p.66Chapter 4.2.3 --- Mass Transit Routes Selected for the Prototype --- p.67Chapter 4.3 --- Data Source and Data Collection --- p.67Chapter 4.4 --- Geographical Data Preparation --- p.71Chapter 4.4.1 --- Data Conversion --- p.73Chapter 4.4.2 --- Geographical Data Input --- p.79Chapter 4.5 --- Attribute Data Input --- p.86Chapter 4.6 --- Summary --- p.88Chapter CHAPTER V --- IMPLEMENTATION OF THE PROTOTYPEChapter 5.1 --- Introduction --- p.89Chapter 5.2 --- Construction of the Route Service Network --- p.89Chapter 5.2.1 --- Generation of the Geographical Network --- p.90Chapter 5.2.2 --- Setting Attribute Data for the Route Service Network --- p.95Chapter 5.3 --- A GIS-T Prototype for the Study Area --- p.102Chapter 5.4 --- General GIS Functions of the Prototype --- p.104Chapter 5.4.1 --- Information Retrieve --- p.104Chapter 5.4.2 --- Display --- p.105Chapter 5.4.3 --- Data Query --- p.105Chapter 5.5 --- Routing in the Prototype --- p.105Chapter 5.5.1 --- Routing Procedure --- p.108Chapter 5.5.2 --- Examples and Results --- p.110Chapter 5.5.3 --- Comparison and Analysis --- p.113Chapter 5.6 --- Summary --- p.118Chapter CHAPTER VI --- CONCLUSIONChapter 6.1 --- Research Findings --- p.123Chapter 6.2 --- Research Limitations --- p.126Chapter 6.3 --- Direction of Further Studies --- p.128BIBLIOGRAPHY --- p.13

    Geo Data Science for Tourism

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    This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

    A spatiotemporal indexing method for disaggregate transportation data

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    Time, location, and attributes are three elements of a GIS, but all commercial GIS packages can only handle location and attributes; they are in fact a static GIS. Spatiotemporal GIS has been a hot research topic recentlySpatiotemporal GIS and its application in transportation research are still prematureThis thesis focuses on spatiotemporal query problems on travel data Specifically, It attempts to answer this question during a time period which trips pass through one or more specific streets? To speed up this spatiotemporal query for large data sets, a spatiotemporal index on the trip data is built by combining Avenue, AML, and C+. All the trip origin ends and those last destination ends for each individual on each day are geocoded using Avenue scripts The trip shortest path route system is created based on ArcInfo dynamic segmentation and network analysis functionsAn array of 2-D tree structures based on each trip\u27s beginning time and ending time and each street traversed are then created in C++ and AvenueThis array of 2-D tree structures is stored in memory. Finally, the spatiotemporal query function is performed by examining the array of 2-D tree structures for a given time window using Avenue and C++. A sample trip log data file in the Knoxville Metropolitan Area and Knox county street shape file are used to implement the spatiotemporal query. This thesis is concluded that efficient indexing methods must be developed to handle complicated spatiotemporal queries for large travel data set

    Geographic Information Systems and Science

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    Geographic information science (GISc) has established itself as a collaborative information-processing scheme that is increasing in popularity. Yet, this interdisciplinary and/or transdisciplinary system is still somewhat misunderstood. This book talks about some of the GISc domains encompassing students, researchers, and common users. Chapters focus on important aspects of GISc, keeping in mind the processing capability of GIS along with the mathematics and formulae involved in getting each solution. The book has one introductory and eight main chapters divided into five sections. The first section is more general and focuses on what GISc is and its relation to GIS and Geography, the second is about location analytics and modeling, the third on remote sensing data analysis, the fourth on big data and augmented reality, and, finally, the fifth looks over volunteered geographic information.info:eu-repo/semantics/publishedVersio
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