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Efficient Generation of Geographically Accurate Transit Maps

Published: 18 September 2019 Publication History

Abstract

We present LOOM (Line-Ordering Optimized Maps), an automatic generator of geographically accurate transit maps. The input to LOOM is data about the lines of a transit network: for each line, its station sequence and geographical course. LOOM proceeds in three stages: (1) construct a line graph, where edges correspond to network segments with the same set of lines following the same course; (2) apply a set of local transformation rules that compute an optimal partial ordering of the lines and speed up the next stage; (3) construct an Integer Linear Program (ILP) that yields a line ordering for each edge and minimizes the total number of line crossings and line separations; and (4) based on the line graph and the computed line ordering, draw the map. As our maps respect the geography of the transit network, they can be used as overlays in typical map services. Previous research either did not take the network geography into account or was only concerned with schematic metro map layouting. We evaluate LOOM on six real-world transit networks, with line-ordering search-space sizes up to 2 × 10267. Using our transformation rules and an improved ILP formulation, we compute optimal line orderings in a fraction of a second for all networks. This enables interactive use of our method in map editors.

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Cited By

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  • (2025)UnDRground Tubes: Exploring Spatial Data with Multidimensional Projections and Set VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345631431:1(196-206)Online publication date: Jan-2025
  • (2024)Multilevel Visual Analysis of Aggregate Geo-NetworksIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.322995330:7(3135-3150)Online publication date: Jul-2024
  • (2024)Large-scale Generation of Transit Maps from OpenStreetMap DataThe Cartographic Journal10.1080/00087041.2024.232576160:4(342-366)Online publication date: 2-Jul-2024
  • Show More Cited By

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Information

Published In

cover image ACM Transactions on Spatial Algorithms and Systems
ACM Transactions on Spatial Algorithms and Systems  Volume 5, Issue 4
December 2019
164 pages
ISSN:2374-0353
EISSN:2374-0361
DOI:10.1145/3361970
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2019
Accepted: 01 May 2019
Revised: 01 March 2019
Received: 01 November 2018
Published in TSAS Volume 5, Issue 4

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Author Tags

  1. Public transit network
  2. graph drawing
  3. graphical optimization
  4. map generation

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Cited By

View all
  • (2025)UnDRground Tubes: Exploring Spatial Data with Multidimensional Projections and Set VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345631431:1(196-206)Online publication date: Jan-2025
  • (2024)Multilevel Visual Analysis of Aggregate Geo-NetworksIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.322995330:7(3135-3150)Online publication date: Jul-2024
  • (2024)Large-scale Generation of Transit Maps from OpenStreetMap DataThe Cartographic Journal10.1080/00087041.2024.232576160:4(342-366)Online publication date: 2-Jul-2024
  • (2020)A Survey on Transit Map Layout – from Design, Machine, and Human PerspectivesComputer Graphics Forum10.1111/cgf.1403039:3(619-646)Online publication date: 18-Jul-2020

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