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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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United States
IEEE
01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1077-2626, 1941-0506, 1941-0506 |
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| Abstract | 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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| AbstractList | 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. Index Terms-Bus route planning, spatial decision-making, urban data visual analytics 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. 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. Index Terms-Bus route planning, spatial decision-making, urban data visual analytics. 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. Index Terms-Bus route planning, spatial decision-making, urban data visual analytics.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. Index Terms-Bus route planning, spatial decision-making, urban data visual analytics. |
| Author | Weng, Di Zheng, Chengbo Zheng, Yu Ma, Mingze Wu, Yingcai Deng, Zikun Xu, Mingliang Bao, Jie |
| Author_xml | – sequence: 1 givenname: Di surname: Weng fullname: Weng, Di email: dweng@zju.edu.cn organization: State Key Lab of CAD&CG, Zhejiang University, China and Zhejiang Lab, Hangzhou, China – sequence: 2 givenname: Chengbo surname: Zheng fullname: Zheng, Chengbo email: chbozheng@gmail.com organization: Zhejiang Lab, Hangzhou, China – sequence: 3 givenname: Zikun surname: Deng fullname: Deng, Zikun email: zikun-rain@zju.edu.cn organization: State Key Lab of CAD&CG, Zhejiang University, China and Zhejiang Lab, Hangzhou, China – sequence: 4 givenname: Mingze surname: Ma fullname: Ma, Mingze email: mamzaug@foxmail.com organization: Zhejiang Lab, Hangzhou, China – sequence: 5 givenname: Jie surname: Bao fullname: Bao, Jie email: baojie@jd.com organization: JD Intelligent Cities Research, JD Intelligent Cities Business Unit, JD Digits, Beijing, China – sequence: 6 givenname: Yu surname: Zheng fullname: Zheng, Yu email: msyuzheng@outlook.com organization: JD Intelligent Cities Research, JD Intelligent Cities Business Unit, JD Digits, Beijing, China – sequence: 7 givenname: Mingliang surname: Xu fullname: Xu, Mingliang email: iexumingliang@zzu.edu.cn organization: School of Information Engineering, Zhengzhou University, Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, China – sequence: 8 givenname: Yingcai surname: Wu fullname: Wu, Yingcai email: ycwu@zju.edu.cn organization: State Key Lab of CAD&CG, Zhejiang University, China and Zhejiang Lab, Hangzhou, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33048743$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
| Title | Towards Better Bus Networks: A Visual Analytics Approach |
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