Analysis of Urban Road Spatiotemporal Situation by Geographically Weighted Regression with Spatial Grid Computing Method
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摘要: 城市化加速发展，交通拥堵已成为全球大城市面临的共同难题。高效、准确的分析与发现交通状态与影响因素的空间变化关系，是优化道路交通要素配置的重要基础。提出了地理加权回归城市道路时空运行态势空间网格化计算方法（ Road Grid Geographically WeightedRegression，RG-GWR），首先以两种尺寸网格嵌套的“九宫格”计算区域路网承载力比率Q，识别出路网配置不均衡区域；然后结合实况交通态势，以地理加权回归模型，计算单元网格的交通时空运行态势影响异质性参数及其回归关系，得到基于网格的邻近区域路网交通要素配置配比，实现以“九宫格”为单元的路网要素优化配置。以成都市为例，构建了3km×3km、1km×1km、1/3km×1/3km三种尺寸空间网格，形成多级叠加的“九宫格”模型，计算提取了两种级别九宫格模型区域承载力参数Q，结果与高德实际路况匹配度分别达到62.5%与87.5%；构建RG-GWR网格模型，不同时段交通态势拟合度达到80%以上。结果表明，本文从空间角度分析道路交通均衡配置高效、可行，具有服务于智能化平台的广阔前景。Abstract: 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 grid model-based road geographically weighted regression (RG-GWR) analysis method is proposed for the first time. The carrying capacity ratio Q of regional road network is calculated by the "nine grid" model composed of two kinds of nested grids. By calculating the ratio Q of the central cell of the nine-grid and analyzing the Q value 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 as an example, three grid models of 3 km×3 km, 1 km×1 km and 1/3 km×1/3 km are constructed. Results: The results match the actual road conditions of AMap by 62.5% and 87.5%. By further analyzing the traffic influencing factors, the 1 km×1 km RG-GWR model is constructed, and the fitting degree of traffic situation in different periods reaches more than 80%. Conclusions: The results show that 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 like Smart city and intelligent transportation.
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Key words:
- electronic map /
- GIS /
- road network carrying capacity /
- grid /
- GWR
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[1] Nanaki E A, Koroneos C J, Roset J, et al. Environmental Assessment of 9 European Public Bus Transportation Systems[J]. Sustainable Cities and Society, 2017, 28: 42-52 [2] Awad W H. Estimating Traffic Capacity for Weaving Segments Using Neural Networks Technique[J]. Applied Soft Computing, 2004, 4(4): 395-404 [3] Brunsdon C, Fotheringham A S, Charlton M E. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity[J]. Geographical Analysis, 2010, 28(4): 281-298 -
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