Towards Better Bus Networks: A Visual Analytics Approach

Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternat...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics Jg. 27; H. 2; S. 817 - 827
Hauptverfasser: Weng, Di, Zheng, Chengbo, Deng, Zikun, Ma, Mingze, Bao, Jie, Zheng, Yu, Xu, Mingliang, Wu, Yingcai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Bus route planning
Buses (vehicles)
Data visualization
Decision analysis
Decision making
Inspection
Knowledge engineering
Mathematical analysis
Planning
Positive feedback
Route planning
Solution space
Spatial data
spatial decision-making
Transportation
Urban areas
urban data visual analytics
Visual analytics
ISSN:1077-2626, 1941-0506, 1941-0506
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Zusammenfassung:Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2020.3030458