A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts

Yunlin Guan,Haonan Guo,Yu Zhou,Xuedong Yan,Yun Wang,Rakesh Mishra

doi:10.1155/2020/8898848

Yunlin Guan, Haonan Guo + Show 4 more

Open Access

https://doi.org/10.1155/2020/8898848

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Abstract

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.

Highlights

As the growth of motor vehicle ownership throughout the world continues to lead to various traffic problems, solutions to mitigate issues of traffic safety, congestion, noise, air pollution, and parking, are becoming increasingly urgent [1, 2]. e majority of previous research studies had focused on the reduction of traffic accidents that alleviated environmental pollution and the relieve of traffic jams, while limited attention had been paid to parking problems [3, 4]
Research has indicated that the average volume of traffic related to parking during peak hours can reach 30–50% of the total traffic [6]. erefore, the formulation of a reasonable parking planning is of great importance to both ease the burden of large road traffic volumes and guarantee an increased level of parking service during peak parking hours [7]
The parking demand estimation is performed with support vector regression (SVR). e model can accurately estimate the parking demand based on partial parking survey data, OD data with peak parking hour attributes, and land use information contained in each parking zone

Summary

Introduction

As the growth of motor vehicle ownership throughout the world continues to lead to various traffic problems, solutions to mitigate issues of traffic safety, congestion, noise, air pollution, and parking, are becoming increasingly urgent [1, 2]. e majority of previous research studies had focused on the reduction of traffic accidents that alleviated environmental pollution and the relieve of traffic jams, while limited attention had been paid to parking problems [3, 4]. The ever-increasing parking demand can no longer be met owing to the limited number of parking lots and land resources To mitigate this problem, parking planning has started to shift to demand management. Traditional parking demand estimation methods based on parking survey data for the entire study area cannot be accurate, as manual errors are recorded and the impact of the increase in private vehicle ownership on the parking demand is unaccounted for. E model can accurately estimate the parking demand based on partial parking survey data, OD data with peak parking hour attributes, and land use information contained in each parking zone. E proposed parking demand estimation framework is based on the relationship between the travel characteristics obtained from modern transportation systems and parking characteristics and can accurately estimate the parking demand via OD big-data resources.

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Results and Discussion