A review of research on urban parking prediction

IF 7.4 2区 工程技术 Q1 ENGINEERING, CIVIL
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引用次数: 0

Abstract

The rapid growth of urban traffic has intensified daily congestion, affecting both traffic flow and parking. Accurate parking prediction plays a vital role in effectively managing limited parking resources and is essential for the successful implementation of advanced intelligent systems. In an effort to comprehensively assess the latest developments in parking prediction, we curated a dataset of 639 articles spanning from 2010 to the present, using the Scopus database. Initially, we performed a bibliometric analysis utilizing VOSviewer software. These findings not only illuminate emerging trends within the parking prediction field but also provide strategic guidance for its progression. Subsequently, we categorized advancements in three focal areas: behavior prediction, demand prediction, and parking space prediction. A comprehensive overview of the present research status and future directions was then provided. The findings underscore the substantial progress achieved in current parking prediction models, achieved through diverse avenues like multi-source data integration, multi-variable feature extraction, nonlinear relationship modeling, deep learning techniques application, and ensemble model utilization. These innovative endeavors have not only pushed the theoretical boundaries of parking prediction but also significantly heightened the precision and applicability of predictive models in practical scenarios. Prospective research should explore avenues such as processing unstructured parking datasets, developing predictive models for small-scale data, mitigating noise interference in parking data, and harnessing potent platform fusion techniques. This study's significance transcends guiding and catalyzing advancement in academic and practical domains; it holds paramount relevance across academic research, technological innovation, decision-making support, business applications, and policy formulation.

城市停车预测研究综述
城市交通的快速增长加剧了日常拥堵,对交通流和停车都造成了影响。准确的停车预测对有效管理有限的停车资源起着至关重要的作用,也是成功实施先进智能系统的关键。为了全面评估停车预测方面的最新进展,我们利用 Scopus 数据库整理了一个包含 639 篇文章的数据集,时间跨度从 2010 年至今。首先,我们利用 VOSviewer 软件进行了文献计量分析。这些发现不仅揭示了停车预测领域的新兴趋势,还为其发展提供了战略指导。随后,我们对三个重点领域的研究进展进行了分类:行为预测、需求预测和停车位预测。然后,我们对研究现状和未来方向进行了全面概述。研究结果强调了当前停车预测模型所取得的实质性进展,这些进展是通过多源数据整合、多变量特征提取、非线性关系建模、深度学习技术应用和集合模型利用等多种途径实现的。这些创新努力不仅拓展了停车预测的理论边界,还大大提高了预测模型在实际场景中的精度和适用性。前瞻性研究应探索处理非结构化停车数据集、开发小规模数据预测模型、减少停车数据中的噪声干扰以及利用强大的平台融合技术等途径。这项研究的意义不仅在于指导和促进学术和实践领域的进步,它在学术研究、技术创新、决策支持、商业应用和政策制定等方面都具有极其重要的现实意义。
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来源期刊
CiteScore
13.60
自引率
6.30%
发文量
402
审稿时长
15 weeks
期刊介绍: The Journal of Traffic and Transportation Engineering (English Edition) serves as a renowned academic platform facilitating the exchange and exploration of innovative ideas in the realm of transportation. Our journal aims to foster theoretical and experimental research in transportation and welcomes the submission of exceptional peer-reviewed papers on engineering, planning, management, and information technology. We are dedicated to expediting the peer review process and ensuring timely publication of top-notch research in this field.
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