Integrating general principles into mixed-integer programming to optimize schematic network maps

Schematic maps are popular for the representation of transport networks. Many automated methods have been developed to generate such maps. In these methods, optimization techniques work with various sets of constraints. Most of these constraints govern geometric properties of individual features. A...

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Veröffentlicht in:International journal of geographical information science : IJGIS Jg. 33; H. 11; S. 2305 - 2333
Hauptverfasser: Lan, Tian, Li, Zhilin, Ti, Peng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 02.11.2019
Taylor & Francis LLC
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ISSN:1365-8816, 1362-3087, 1365-8824
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Abstract Schematic maps are popular for the representation of transport networks. Many automated methods have been developed to generate such maps. In these methods, optimization techniques work with various sets of constraints. Most of these constraints govern geometric properties of individual features. A few constraints address relationships among features, but none explicitly deal with the main structure of an entire network. We believe that preservation of main structure is the most important and preservation of relative relations is helpful. This is because human perception follows a global-to-local process. These constraints have recently been formed into four general principles, with two for global structure and two for relativity of features. This study develops an automated method by integrating these principles into the mixed-integer programming (MIP) framework. Experimental evaluations have been conducted with two sets of real-world transit networks. In comparison to the existing method, the proposed method has smaller fractal dimensions, better computational performance and higher scores in terms of clarity, recognition of major lines, visual simplicity and satisfaction. Therefore, it is concluded that the proposed method can generate schematic maps with improved clarity and aesthetics. The idea in this study is also helpful for the design of other visual representations.
AbstractList Schematic maps are popular for the representation of transport networks. Many automated methods have been developed to generate such maps. In these methods, optimization techniques work with various sets of constraints. Most of these constraints govern geometric properties of individual features. A few constraints address relationships among features, but none explicitly deal with the main structure of an entire network. We believe that preservation of main structure is the most important and preservation of relative relations is helpful. This is because human perception follows a global-to-local process. These constraints have recently been formed into four general principles, with two for global structure and two for relativity of features. This study develops an automated method by integrating these principles into the mixed-integer programming (MIP) framework. Experimental evaluations have been conducted with two sets of real-world transit networks. In comparison to the existing method, the proposed method has smaller fractal dimensions, better computational performance and higher scores in terms of clarity, recognition of major lines, visual simplicity and satisfaction. Therefore, it is concluded that the proposed method can generate schematic maps with improved clarity and aesthetics. The idea in this study is also helpful for the design of other visual representations.
Author Ti, Peng
Li, Zhilin
Lan, Tian
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Snippet Schematic maps are popular for the representation of transport networks. Many automated methods have been developed to generate such maps. In these methods,...
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SubjectTerms Automation
Clarity
Computer applications
Fractal geometry
general principles for schematic maps
Integer programming
Integers
mixed-integer programming (MIP)
Optimization
Optimization techniques
Preservation
Principles
Relativity
Representations
Schematic network maps
Transportation networks
Transportation planning
Title Integrating general principles into mixed-integer programming to optimize schematic network maps
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