Analysis of Urban Road Spatiotemporal Situation by Geographically Weighted Regression with Spatial Grid Computing Method

被引：0
|
作者：
Jiang D. [1 ,2 ]
Zhao W. [1 ]
Wang Y. [1 ]
Wan B. [2 ]
机构：
[1] College of Resources, Environment and Tourism, Capital Normal University, Beijing
[2] Chinese Society for Urban Studies, National Smart City Joint Laboratory, Beijing
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2023年 / 48卷 / 06期
关键词：
electronic map; geographically weighted regression(GWR); GIS; grid; road network carrying capacity;
D O I：
10.13203/j.whugis20210173
中图分类号：
学科分类号：
摘要：
Objectives: With the rapid development of urbanization, traffic congestion has become a common problem faced by big cities all over the world. Scientific analysis of road network carrying capacity and traffic impact factors is a prerequisite for optimizing the spatial allocation of road traffic factors. How to give full play to the advantages of spatial information technology, efficiently and accurately analyze the balance of regional road network carrying capacity, and find the spatial change relationship between traffic state and influencing factors, is very important for alleviating urban traffic congestion. Methods: The road grid geographically weighted regression (RG-GWR) analysis method is proposed. The carrying capacity ratio of regional road network is calculated by the nine-grids model composed of two kinds of nested grids. By calculating the carrying capacity ratio of the central cell of the nine grids and analyzing the ratio according to the law of conservation of flow, the unbalanced area of road network configuration is identified. By analyzing the regression relationship between the grid cell traffic situation and the influencing factors, the traffic space-time operation situation is obtained. Taking Chengdu city as an example, three grid models with different sizes are constructed. Results: The results match the actual road conditions of AMap by 62.5% and 87.5%, respectively. By further analyzing the traffic influencing factors, the fitting degree of RG-GWR model in traffic situation of different periods could be more than 80%. Conclusions: The grid model is an efficient and feasible method to analyze the road network carrying capacity and traffic impact factors from the perspective of space and has a broad prospect to serve the intelligent platform and intelligent transportation. © 2023 Wuhan University. All rights reserved.
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页码：988 / 996
页数：8
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