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JIANG Dong, ZHAO Wenji, WANG Yanhui, WAN Biyu. Analysis of Urban Road Spatiotemporal Situation by Geographically Weighted Regression with Spatial Grid Computing Method[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210173
Citation: JIANG Dong, ZHAO Wenji, WANG Yanhui, WAN Biyu. Analysis of Urban Road Spatiotemporal Situation by Geographically Weighted Regression with Spatial Grid Computing Method[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210173

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

doi: 10.13203/j.whugis20210173
Funds:

The National Basic Work of Science and Technology of China (2018YFB2101400,2018YFC0706000)

  • Received Date: 2022-05-10
    Available Online: 2022-05-20
  • 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.
  • [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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Analysis of Urban Road Spatiotemporal Situation by Geographically Weighted Regression with Spatial Grid Computing Method

doi: 10.13203/j.whugis20210173
Funds:

The National Basic Work of Science and Technology of China (2018YFB2101400,2018YFC0706000)

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.

JIANG Dong, ZHAO Wenji, WANG Yanhui, WAN Biyu. Analysis of Urban Road Spatiotemporal Situation by Geographically Weighted Regression with Spatial Grid Computing Method[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210173
Citation: JIANG Dong, ZHAO Wenji, WANG Yanhui, WAN Biyu. Analysis of Urban Road Spatiotemporal Situation by Geographically Weighted Regression with Spatial Grid Computing Method[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210173
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