Towards Better Bus Networks: A Visual Analytics Approach
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 2; 2025: 2.
8 papers.
- 2021VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Modelscited by 12
- 2024CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent Systemcited by 3
- 2024TrafPS: A Shapley-based Visual Analytics Approach to Interpret Trafficcited by 1
- 2022TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapleycited by 0
- 2022DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graphcited by 0
- 2023A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimizationcited by 0
- 2025VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Designcited by 0
- 2025Exploring Urban Factors with Autoencoders: Relationship Between Static and Dynamic Featurescited by 0
- 2025Exploring Urban Factors with Autoencoders: Relationship Between Static and Dynamic Features7 Sep 2025
- 2025VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design10 Aug 2025
- 2024CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System23 May 2024
- 2024TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic7 Mar 2024
- 2023A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization10 Aug 2023
- 2022DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graph26 May 2022
- 2022TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley11 Mar 2022
- 2021VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models4 Aug 2021