Leveraging business intelligence solutions for urban parking management

Q1 Social Sciences City, Culture and Society Pub Date : 2024-04-26 DOI:10.1016/j.ccs.2024.100579
Mohammad Mahdi Ahmadian , Douglas Baker , Alexander Paz
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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.

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利用商业智能解决方案进行城市停车管理
高效的停车管理对于提升客户体验、流动性、可达性和城市地区的整体生活质量至关重要。近年来,停车分析已成为了解驾驶员行为和制定数据驱动管理策略的重要工具。然而,数据提取、转换、加载和分析的复杂性往往阻碍了这些工具的应用。此外,这些工具的实施既耗时又昂贵,进一步限制了它们在运营商和城市当局中的实际应用。为解决这一问题,本文介绍了商业智能工具的开发情况,该工具专门设计用于通过交易数据在收集、处理和分析系统之间的自动流动来促进停车管理。该工具提供了易于使用的分析功能,使停车场管理人员能够分析停车场交易数据、识别趋势和模式,并快速、轻松地做出有关停车场管理的明智决策。该工具的实施具有成本效益,为管理城市地区的路边停车提供了宝贵的解决方案。这项研究凸显了商业智能工具在停车管理方面的潜力,有助于提高停车管理的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
City, Culture and Society
City, Culture and Society Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
4.00
自引率
0.00%
发文量
26
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