Leveraging Business Intelligence Solutions for Urban Parking Management
Open access copy at publisher website
Description
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.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
ID Code: | 248135 | ||||||
---|---|---|---|---|---|---|---|
Item Type: | Contribution to Journal (Journal Article) | ||||||
Refereed: | Yes | ||||||
ORCID iD: |
|
||||||
Measurements or Duration: | 10 pages | ||||||
Keywords: | On-street parking, visualization, business intelligence, urban analytics, parking occupancy, parking transactions | ||||||
DOI: | 10.1016/j.ccs.2024.100579 | ||||||
ISSN: | 1877-9166 | ||||||
Pure ID: | 167133429 | ||||||
Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Architecture & Built Environment Current > Schools > School of Civil & Environmental Engineering |
||||||
Funding Information: | This research is funded by iMOVE CRC (project 3-025) and supported by the Cooperative Research Centers program, an Australian Government initiative. | ||||||
Copyright Owner: | Crown 2024 | ||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||
Deposited On: | 06 Jun 2024 23:32 | ||||||
Last Modified: | 09 Jun 2024 21:04 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page