Comparative Study of Approaches to Delineating Built-Up Areas Using Road Network Data

2015 ◽  
Vol 19 (6) ◽  
pp. 848-876 ◽  
Author(s):  
Qi Zhou
2011 ◽  
Vol 14 (4) ◽  
pp. 389-413 ◽  
Author(s):  
Tao Cheng ◽  
James Haworth ◽  
Jiaqiu Wang

2020 ◽  
Author(s):  
Ana Laura Costa ◽  
Elco Koks ◽  
Kees van Ginkel ◽  
Frederique de Groen ◽  
Lorenzo Alfieri ◽  
...  

<p>River flooding is among the most profound climate hazards in Europe and poses a threat to its road transport infrastructure. Traditional continental-scale flood risk studies do not accurately capture these disruptions because they are typically grid-based, whereas roads are relatively narrow line elements which are therefore omitted. Moreover, these grid-approaches disregard the network properties of roads, whereas the costs of reduced mobility could largely exceed the costs of the physical damage to the infrastructure.</p><p>We address these issues by proposing and applying an improved physical damage assessment coupled with the assessment of mobility disruption for a comprehensive risk assessment at a continental level.</p><p>In this study, we introduce an object-based, continental scale flood risk assessment of the European road network. We improve the estimates of direct, physical damage, by drawing road network data from OpenStreetMap, while making optimal use of the available metadata. We also introduce a set of road-specific flood damage functions, which are validated for an observed flood event in Germany. The results of this approach are compared to the traditional, grid-based approach to modelling road transport damage.</p><p>Next, we showcase how the object-based approach can be used to study potential mobility disruptions. In this study we present how the network data from OpenStreetMap and available metadata can be used to assess the flood impacts in terms of decreased connectivity, that is, increased distance, time and/or costs. The approach is flexible in physical scope, able to address national and continental resilience assessments and provide advice on tipping points of service performance. Furthermore, flexibility is also incorporated in terms of different resilience perspectives including decision-making by the asset owner or the national or trans-national supply chain disruption to a particular economic sector or company.</p><p>Finally, the risk assessment is discussed based on applications for the impacts of floods on European roads and the potential to extend to multi-hazard assessments (landslides, earthquakes, pluvial flooding) and other types of critical networks is discussed.  </p><p> </p><p>This paper has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776479 for the project CO-designing the Assessment of Climate CHange costs. https://www.coacch.eu/</p><p> </p>


2012 ◽  
Vol 214 ◽  
pp. 749-754
Author(s):  
Xiong He ◽  
Yi Yang Gao ◽  
Tao Chen

This Paper Introduces a Method of Designing and Organizing Road Network Data and Clarifies Algorithm Based on Layering Search, Suitable for Computing the Routes of Vehicle Navigation in Big Districts. the Algorithm Is Calculated in the High Grade Road Network, and then in the Local Refinement. the Method Is to Get a Point in the Calculated High Grade Route and then Calculate the Optimal Route from the Start Point to the Point (the Selected Point Should Be a Node near to the End), so Does the End Point. the Algorithm Was Applied to the Routes Planning and the Experimental Results Show that the Use of Data Structure and Algorithm Saves Storage Space and Greatly Improves the Calculation Efficiency.


Author(s):  
Mohammed A. Quddus

Map matching algorithms integrate positioning data with spatial road network data to support the navigation modules of intelligent transport systems requiring location and navigation data. Research on the development of map matching algorithms has significantly advanced over the last few years. This article looks at different methods that have been adopted in map matching algorithms and highlights future trends in map matching and navigation research.


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