BRPVis: Visual Analytics for Bus Route Planning Based on Perception of Passenger Travel Demand.

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Qiushi Xia, Huijie Zhang, Dezhan Qu, Jinghan Bai, Cheng Lv
{"title":"BRPVis: Visual Analytics for Bus Route Planning Based on Perception of Passenger Travel Demand.","authors":"Qiushi Xia, Huijie Zhang, Dezhan Qu, Jinghan Bai, Cheng Lv","doi":"10.1109/MCG.2024.3454645","DOIUrl":null,"url":null,"abstract":"<p><p>Bus route planning is a complex application problem within the transportation domain, aiming to identify the best route among numerous candidate solutions. Despite existing research significantly reducing the exploration space of solutions, planners still face challenges in further exploring optimal route planning solutions. Specifically, the diversity of route attributes increases the complexity of determining their impact, such as the variety and quantity of reachable points of interest. Therefore, we present BRPVis, an interactive visual analytics system designed to assist bus route planners in exploring optimal solutions through multi-level visualization and rich interaction design. Furthermore, we propose a human-machine collaborative multicriteria decision-making method, which quantitatively analyzes the weights of route attributes while incorporating interactive feedback mechanisms to support personalized route exploration. Based on exploration using real-world traffic datasets, three case studies conducted with domain experts demonstrate that BRPVis effectively provides decision support for bus route planning tasks.</p>","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Computer Graphics and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/MCG.2024.3454645","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

Bus route planning is a complex application problem within the transportation domain, aiming to identify the best route among numerous candidate solutions. Despite existing research significantly reducing the exploration space of solutions, planners still face challenges in further exploring optimal route planning solutions. Specifically, the diversity of route attributes increases the complexity of determining their impact, such as the variety and quantity of reachable points of interest. Therefore, we present BRPVis, an interactive visual analytics system designed to assist bus route planners in exploring optimal solutions through multi-level visualization and rich interaction design. Furthermore, we propose a human-machine collaborative multicriteria decision-making method, which quantitatively analyzes the weights of route attributes while incorporating interactive feedback mechanisms to support personalized route exploration. Based on exploration using real-world traffic datasets, three case studies conducted with domain experts demonstrate that BRPVis effectively provides decision support for bus route planning tasks.

BRPVis:基于乘客出行需求感知的公交线路规划可视化分析。
公交线路规划是交通领域的一个复杂应用问题,旨在从众多候选方案中找出最佳路线。尽管现有研究大大缩小了解决方案的探索空间,但规划者在进一步探索最佳路线规划解决方案时仍面临挑战。具体来说,路线属性的多样性增加了确定其影响的复杂性,例如可到达兴趣点的种类和数量。因此,我们提出了一个交互式可视化分析系统 BRPVis,旨在通过多层次可视化和丰富的交互设计,帮助公交线路规划人员探索最佳解决方案。此外,我们还提出了一种人机协作多标准决策方法,该方法可定量分析路线属性的权重,同时结合互动反馈机制来支持个性化路线探索。基于使用真实世界交通数据集进行的探索,与领域专家共同开展的三项案例研究表明,BRPVis 能有效地为公交线路规划任务提供决策支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Computer Graphics and Applications
IEEE Computer Graphics and Applications 工程技术-计算机:软件工程
CiteScore
3.20
自引率
5.60%
发文量
160
审稿时长
>12 weeks
期刊介绍: IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics, visualization, virtual and augmented reality, and HCI. From specific algorithms to full system implementations, CG&A offers a unique combination of peer-reviewed feature articles and informal departments. Theme issues guest edited by leading researchers in their fields track the latest developments and trends in computer-generated graphical content, while tutorials and surveys provide a broad overview of interesting and timely topics. Regular departments further explore the core areas of graphics as well as extend into topics such as usability, education, history, and opinion. Each issue, the story of our cover focuses on creative applications of the technology by an artist or designer. Published six times a year, CG&A is indispensable reading for people working at the leading edge of computer-generated graphics technology and its applications in everything from business to the arts.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信