A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts,Journal of Advanced Transportation

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A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts
Journal of Advanced Transportation ( IF 2.3 ) Pub Date : 2020-12-29 , DOI: 10.1155/2020/8898848
Yunlin Guan ₁ , Yun Wang ₁ , Xuedong Yan ₁ , Haonan Guo ₁ , Yu Zhou ₂

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Parking planning is a key issue in the process of urban transportation planning. To formulate a high-quality planning scheme, an accurate estimate of the parking demand is critical. Most previous published studies were based primarily on parking survey data, which is both costly and inaccurate. Owing to limited data sources and simplified models, most of the previous research estimates the parking demand without consideration for the relationship between parking demand, land use, and traffic attributes, thereby causing a lack of accuracy. Thus, this study proposes a big-data-driven framework for parking demand estimation. The framework contains two steps. The first step is the parking zone division method, which is based on the statistical information grid and multidensity clustering algorithms. The second step is parking demand estimation, which is extracted by support vector machines posed in the form of a machine learning regression problem. The framework is evaluated using a case in the city center in Cangzhou, China.

更新日期：2020-12-29

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