4 Search Results for "Urban, Marie"

Document
DOI: 10.4230/LIPIcs.GIScience.2021.II.3
Comparison of Simulated Fast and Green Routes for Cyclists and Pedestrians

Authors: Christina Ludwig, Sven Lautenbach, Eva-Marie Schömann, and Alexander Zipf

Published in: LIPIcs, Volume 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II

Abstract
Routes with a high share of greenery are attractive for cyclist and pedestrians. We analyze how strongly such green routes differ from the respective fast routes using the openrouteservice. Greenness of streets was estimated based on OpenStreetMap data in combination with Sentinel-II imagery, 3d laser scan data and administrative information on trees on public ground. We assess the effect both at the level of the individual route and at the urban level for two German cities: Dresden and Heidelberg. For individual routes, we study how strongly green routes differ from the respective fast routes. In addition, we identify parts of the road network which represent important green corridors as well as unattractive parts which can or cannot be avoided at the cost of reasonable detours. In both cities, our results show the importance of urban green spaces for the provision of attractive green routes and provide new insights for urban planning by identifying unvegetated bottlenecks in the street network for which no green alternatives exist at this point.
Cite as

Christina Ludwig, Sven Lautenbach, Eva-Marie Schömann, and Alexander Zipf. Comparison of Simulated Fast and Green Routes for Cyclists and Pedestrians. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part II. Leibniz International Proceedings in Informatics (LIPIcs), Volume 208, pp. 3:1-3:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{ludwig_et_al:LIPIcs.GIScience.2021.II.3,
  author =	{Ludwig, Christina and Lautenbach, Sven and Sch\"{o}mann, Eva-Marie and Zipf, Alexander},
  title =	{{Comparison of Simulated Fast and Green Routes for Cyclists and Pedestrians}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
  pages =	{3:1--3:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-208-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{208},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.II.3},
  URN =		{urn:nbn:de:0030-drops-147622},
  doi =		{10.4230/LIPIcs.GIScience.2021.II.3},
  annote =	{Keywords: Routing, OpenStreetMap, route choice, urban vegetation, sustainable mobility}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.58
Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer (Short Paper)

Authors: Kelly Sims, Gautam Thakur, Kevin Sparks, Marie Urban, Amy Rose, and Robert Stewart

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Geo-grid algorithms divide a large polygon area into several smaller polygons, which are important for studying or executing a set of operations on underlying topological features of a map. The current geo-grid algorithms divide a large polygon in to a set of smaller but equal size polygons only (e.g. is ArcMaps Fishnet). The time to create a geo-grid is typically proportional to number of smaller polygons created. This raises two problems - (i) They cannot skip unwanted areas (such as water bodies, given about 71% percent of the Earth's surface is water-covered); (ii) They are incognizant to any underlying feature set that requires more deliberation. In this work, we propose a novel dynamically spaced geo-grid segmentation algorithm that overcomes these challenges and provides a computationally optimal output for borderline cases of an uneven polygon. Our method uses an underlying topological feature of population distributions, from the LandScan Global 2016 dataset, for creating grids as a function of these weighted features. We benchmark our results against available algorithms and found our approach improves geo-grid creation. Later on, we demonstrate the proposed approach is more effective in harvesting Points of Interest data from a crowd-sourced platform.
Cite as

Kelly Sims, Gautam Thakur, Kevin Sparks, Marie Urban, Amy Rose, and Robert Stewart. Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 58:1-58:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{sims_et_al:LIPIcs.GISCIENCE.2018.58,
  author =	{Sims, Kelly and Thakur, Gautam and Sparks, Kevin and Urban, Marie and Rose, Amy and Stewart, Robert},
  title =	{{Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{58:1--58:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.58},
  URN =		{urn:nbn:de:0030-drops-93860},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.58},
  annote =	{Keywords: geofence, geo-grid, quadtree, points of interest (POI), volunteered geographic information (VGI)}
}
Document
DOI: 10.4230/OASIcs.ATMOS.2015.16
Speedups for Multi-Criteria Urban Bicycle Routing

Authors: Jan Hrncir, Pavol Zilecky, Qing Song, and Michal Jakob

Published in: OASIcs, Volume 48, 15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2015)

Abstract
Increasing the adoption of cycling is crucial for achieving more sustainable urban mobility. Navigating larger cities on a bike is, however, often challenging due to cities’ fragmented cycling infrastructure and/or complex terrain topology. Cyclists would thus benefit from intelligent route planning that would help them discover routes that best suit their transport needs and preferences. Because of the many factors cyclists consider in deciding their routes, employing multi-criteria route search is vital for properly accounting for cyclists’ route-choice criteria. Direct application of optimal multi-criteria route search algorithms is, however, not feasible due to their prohibitive computational complexity. In this paper, we therefore propose several heuristics for speeding up multi-criteria route search. We evaluate our method on a real-world cycleway network and show that speedups of up to four orders of magnitude over the standard multi-criteria label-setting algorithm are possible with a reasonable loss of solution quality. Our results make it possible to practically deploy bicycle route planners capable of producing high-quality route suggestions respecting multiple real-world route-choice criteria.
Cite as

Jan Hrncir, Pavol Zilecky, Qing Song, and Michal Jakob. Speedups for Multi-Criteria Urban Bicycle Routing. In 15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2015). Open Access Series in Informatics (OASIcs), Volume 48, pp. 16-28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@InProceedings{hrncir_et_al:OASIcs.ATMOS.2015.16,
  author =	{Hrncir, Jan and Zilecky, Pavol and Song, Qing and Jakob, Michal},
  title =	{{Speedups for Multi-Criteria Urban Bicycle Routing}},
  booktitle =	{15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2015)},
  pages =	{16--28},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-99-6},
  ISSN =	{2190-6807},
  year =	{2015},
  volume =	{48},
  editor =	{Italiano, Giuseppe F. and Schmidt, Marie},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2015.16},
  URN =		{urn:nbn:de:0030-drops-54584},
  doi =		{10.4230/OASIcs.ATMOS.2015.16},
  annote =	{Keywords: bicycle routing, multi-criteria shortest path, heuristic speedups}
}
Document
DOI: 10.4230/OASIcs.ATMOS.2015.68
Robust Routing in Urban Public Transportation: Evaluating Strategies that Learn From the Past

Authors: Katerina Böhmová, Matúš Mihalák, Peggy Neubert, Tobias Pröger, and Peter Widmayer

Published in: OASIcs, Volume 48, 15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2015)

Abstract
Given an urban public transportation network and historic delay information, we consider the problem of computing reliable journeys. We propose new algorithms based on our recently presented solution concept (Böhmová et al., ATMOS 2013), and perform an experimental evaluation using real-world delay data from Zürich, Switzerland. We compare these methods to natural approaches as well as to our recently proposed method which can also be used to measure typicality of past observations. Moreover, we demonstrate how this measure relates to the predictive quality of the individual methods. In particular, if the past observations are typical, then the learning- based methods are able to produce solutions that perform well on typical days, even in the presence of large delays.
Cite as

Katerina Böhmová, Matúš Mihalák, Peggy Neubert, Tobias Pröger, and Peter Widmayer. Robust Routing in Urban Public Transportation: Evaluating Strategies that Learn From the Past. In 15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2015). Open Access Series in Informatics (OASIcs), Volume 48, pp. 68-81, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@InProceedings{bohmova_et_al:OASIcs.ATMOS.2015.68,
  author =	{B\"{o}hmov\'{a}, Katerina and Mihal\'{a}k, Mat\'{u}\v{s} and Neubert, Peggy and Pr\"{o}ger, Tobias and Widmayer, Peter},
  title =	{{Robust Routing in Urban Public Transportation: Evaluating Strategies that Learn From the Past}},
  booktitle =	{15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2015)},
  pages =	{68--81},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-99-6},
  ISSN =	{2190-6807},
  year =	{2015},
  volume =	{48},
  editor =	{Italiano, Giuseppe F. and Schmidt, Marie},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2015.68},
  URN =		{urn:nbn:de:0030-drops-54542},
  doi =		{10.4230/OASIcs.ATMOS.2015.68},
  annote =	{Keywords: public transportation, route planning, robustness, optimization, experiments}
}
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