Mohammad Mahdi Ahmadian , Douglas Baker , Alexander Paz
{"title":"Leveraging business intelligence solutions for urban parking management","authors":"Mohammad Mahdi Ahmadian , Douglas Baker , Alexander Paz","doi":"10.1016/j.ccs.2024.100579","DOIUrl":null,"url":null,"abstract":"<div><p>Efficient parking management is essential for enhancing customer experience, mobility, accessibility, and overall quality of life in urban areas. In recent years, parking analytics have emerged as valuable tools for understanding drivers’ behavior and developing data-driven management strategies. However, the application of these tools is often hindered by the complexity of data extraction, transformation, loading and analysis. Additionally, the implementation of these tools can be time-consuming and costly, further limiting their practical use for operators and urban authorities. To address this issue, this paper presents the development of a Business Intelligence tool specifically designed to facilitate parking management through the automated flow of transaction data between collection, processing, and analysis systems. The tool provides easy-to-use analytical capabilities that allow parking managers to analyze parking transaction data, identify trends and patterns, and make informed decisions about parking management quickly and easily. The cost-effective implementation of this tool presents a valuable solution for managing on-street parking in urban areas. This study highlights the potential of Business Intelligence tools for parking management and contributes to improving the effectiveness of parking management.</p></div>","PeriodicalId":39061,"journal":{"name":"City, Culture and Society","volume":"37 ","pages":"Article 100579"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877916624000146/pdfft?md5=9a7e3d13b3b82b12bb91314fe5dd67af&pid=1-s2.0-S1877916624000146-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"City, Culture and Society","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877916624000146","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
Efficient parking management is essential for enhancing customer experience, mobility, accessibility, and overall quality of life in urban areas. In recent years, parking analytics have emerged as valuable tools for understanding drivers’ behavior and developing data-driven management strategies. However, the application of these tools is often hindered by the complexity of data extraction, transformation, loading and analysis. Additionally, the implementation of these tools can be time-consuming and costly, further limiting their practical use for operators and urban authorities. To address this issue, this paper presents the development of a Business Intelligence tool specifically designed to facilitate parking management through the automated flow of transaction data between collection, processing, and analysis systems. The tool provides easy-to-use analytical capabilities that allow parking managers to analyze parking transaction data, identify trends and patterns, and make informed decisions about parking management quickly and easily. The cost-effective implementation of this tool presents a valuable solution for managing on-street parking in urban areas. This study highlights the potential of Business Intelligence tools for parking management and contributes to improving the effectiveness of parking management.