基于双重引力模型的城际客流空间模式探究
Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models
AI总结：
针对部分数据缺失下城际客流建模与交通网络深层结构揭示问题，本文用双重引力模型和腾讯位置大数据分析京津冀城际客流，验证了模型有效性，揭示了分形特性与城际联系变化趋势，提供了分形维数估算新方法。
摘要：
交通网络中的客流反映了城市系统中的空间相互作用模式。引力模型可用于定量描述和预测空间流。然而，在存在部分数据缺失的情况下，如何对客流建模并揭示城市与交通网络的深层结构，仍是一个有待解决的问题。本文致力于借助双重引力模型和腾讯位置大数据，刻画中国京津冀地区的城际客流特征，参数估计方法为最小二乘回归。主要研究结果如下：第一，铁路和公路客流均可通过双重引力模型得到有效描述，少量缺失的空间数据可由预测值补全。第二，能够揭示交通流的分形特性，铁路客流比公路客流更符合引力标度律。第三，预测残差反映了近年来研究区域城际联系的变化趋势，空间动态的重心似乎从京津唐三角地带向保定-石家庄轴线转移。研究得出结论：双重引力模型是分析交通网络及流的空间结构与动态的有效工具，此外，该模型还为估算交通网络的分形维数和空间流模式提供了一种新方法。
英文摘要：
Passenger flows in a traffic network reflect spatial interaction patterns in an urban systems. Gravity models can be employed to quantitatively describe and predict spatial flows. However, how to model passenger flows and reveal the deep structure of urban and traffic networks in the case of missing partial data is still a problem to be solved. This paper is devoted to characterizing the interurban passenger flows in the Beijing-Tianjin-Hebei region of China by means of dual gravity models and Tencent location big data. The method of parameter estimation is the least squares regression. The main results are as follows. First, both railway and highway passenger flows can be effectively described by the dual gravity model. A small part of missing spatial data can be made up by the predicted values. Second, the fractal properties of traffic flows can be revealed. The railway passenger flows more follow gravity scaling law than the highway passenger flows. Third, the prediction residuals indicate the changing trend of interurban connections in the study area in recent years. The center of gravity of spatial dynamics seems to shift from the Beijing-Tianjin-Tangshan triangle to the Beijing-Baoding-Shijiazhuang axis. A conclusion can be reached that the dual gravity models is an effective tools of for analyzing spatial structures and dynamics of traffic networks and flows. Moreover, the models provide a new approach to estimate fractal dimension of traffic network and spatial flow patterns.
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