Bus Operation Safety Business Intelligence Solution: Applying Analytics for Key Performance Indicator, Investigation, and Targeted Actions Analyses with a Centralized Data Warehouse

IF 1.6 4区 工程技术 Q3 ENGINEERING, CIVIL Transportation Research Record Pub Date : 2023-11-09 DOI:10.1177/03611981231205882
Xiaotong Ding, John Maleyeff, Frans Valk
{"title":"Bus Operation Safety Business Intelligence Solution: Applying Analytics for Key Performance Indicator, Investigation, and Targeted Actions Analyses with a Centralized Data Warehouse","authors":"Xiaotong Ding, John Maleyeff, Frans Valk","doi":"10.1177/03611981231205882","DOIUrl":null,"url":null,"abstract":"A business intelligence (BI) system was designed to create a proactive, reactive, and precursor decision tool to improve safety of bus operations at the Massachusetts Bay Transportation Authority (MBTA). The system’s development was motivated in part by recommendations of a safety inspection by the Federal Transportation Administration, and the realization that the MBTA has access to vest amounts of data that are not being used to create information effectively. The developers reduced the complexity of the BI system by incorporating analytics to determine the areas of focus where safety has the most impact. Challenges were posed by the need to integrate internal and external databases with various formats and structures, and the desire to create a self-service BI solution for multiple MBTA users. The resulting BI solution includes applications that support key performance indicator analyses, root cause investigations, and targeted improvement actions. These applications represent an enhanced approach that combines data for a more holistic analysis and eliminates the need to transfer datasets or data analyses by email. The system includes various forms of visualizations to help users navigate the myriad of information, including geospatial maps, interactive pie, line, and bar charts, and word frequency and relationship mapping. The paper details the system’s development and how analytical approaches were used to expose important information that is hidden in the data. Examples of applications are shown using screenshots and a general workflow is presented that could be applicable to agencies with fewer resources.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":"172 ","pages":"0"},"PeriodicalIF":1.6000,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Record","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/03611981231205882","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0

Abstract

A business intelligence (BI) system was designed to create a proactive, reactive, and precursor decision tool to improve safety of bus operations at the Massachusetts Bay Transportation Authority (MBTA). The system’s development was motivated in part by recommendations of a safety inspection by the Federal Transportation Administration, and the realization that the MBTA has access to vest amounts of data that are not being used to create information effectively. The developers reduced the complexity of the BI system by incorporating analytics to determine the areas of focus where safety has the most impact. Challenges were posed by the need to integrate internal and external databases with various formats and structures, and the desire to create a self-service BI solution for multiple MBTA users. The resulting BI solution includes applications that support key performance indicator analyses, root cause investigations, and targeted improvement actions. These applications represent an enhanced approach that combines data for a more holistic analysis and eliminates the need to transfer datasets or data analyses by email. The system includes various forms of visualizations to help users navigate the myriad of information, including geospatial maps, interactive pie, line, and bar charts, and word frequency and relationship mapping. The paper details the system’s development and how analytical approaches were used to expose important information that is hidden in the data. Examples of applications are shown using screenshots and a general workflow is presented that could be applicable to agencies with fewer resources.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
总线运行安全商业智能解决方案:使用集中数据仓库对关键性能指标、调查和目标行动分析进行分析
设计了一个商业智能(BI)系统,用于创建一个主动、被动和先行的决策工具,以提高马萨诸塞湾交通管理局(MBTA)公交运营的安全性。开发该系统的部分动机是联邦运输管理局(Federal Transportation Administration)的安全检查建议,以及MBTA可以访问大量数据,而这些数据没有被有效地用于创建信息。开发人员通过结合分析来确定安全影响最大的重点领域,从而降低了BI系统的复杂性。挑战来自于需要集成具有各种格式和结构的内部和外部数据库,以及为多个MBTA用户创建自助BI解决方案的愿望。最终的BI解决方案包括支持关键性能指标分析、根本原因调查和目标改进行动的应用程序。这些应用程序代表了一种增强的方法,将数据结合起来进行更全面的分析,并消除了通过电子邮件传输数据集或数据分析的需要。该系统包括各种形式的可视化,以帮助用户浏览大量的信息,包括地理空间地图、交互式饼状图、线形图和条形图,以及词频和关系映射。本文详细介绍了该系统的开发以及如何使用分析方法来揭示隐藏在数据中的重要信息。应用程序示例使用屏幕截图显示,并提供了适用于资源较少的机构的一般工作流程。
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 去求助
来源期刊
Transportation Research Record
Transportation Research Record 工程技术-工程:土木
CiteScore
3.20
自引率
11.80%
发文量
918
审稿时长
4.2 months
期刊介绍: Transportation Research Record: Journal of the Transportation Research Board is one of the most cited and prolific transportation journals in the world, offering unparalleled depth and breadth in the coverage of transportation-related topics. The TRR publishes approximately 70 issues annually of outstanding, peer-reviewed papers presenting research findings in policy, planning, administration, economics and financing, operations, construction, design, maintenance, safety, and more, for all modes of transportation. This site provides electronic access to a full compilation of papers since the 1996 series.
期刊最新文献
Acute Bilateral Optic Neuropathy: A Rare Presentation of Wernicke Encephalopathy. Simulation Study of Pedestrians Evacuation Considering Aggressive Behavior Analyzing Freeway Safety Influencing Factors Using the CatBoost Model and Interpretable Machine-Learning Framework, SHAP Numerical Analysis of Error From Sampling of Alternatives in Logit-Based Demand Forecasting Models with Massive Choice Sets Bus Operation Safety Business Intelligence Solution: Applying Analytics for Key Performance Indicator, Investigation, and Targeted Actions Analyses with a Centralized Data Warehouse
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1