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为了满足密集城镇群客流分布预测的需求,为密集城镇群规划及建设提供更加合理的参考,结合密集城镇群客流分布预测对应的研究区域空间跨度较大的特点,将研究区域分为市区、市区外区域2个层级,分别进行交通小区划分。采用适用性较广的最大熵模型进行客流分布预测,同时考虑到空间距离对密集城镇群客流分布的影响权重较大,借鉴重力分布模型的思想,引入广义出行费用反映交通阻抗,对最大熵模型进行修正;并分别从市区、市域2个层级构建双层最大熵模型。采用双层最大熵模型对东莞市2020年客流分布进行了预测。预测结果表明:双层最大熵模型能够减少空间范围过大的不利影响,同时双层最大熵模型相对独立,可以互为补充,更加适用于密集城镇群的客流分布预测。
In order to meet the demand of densely populated urban agglomeration passenger flow distribution forecast and provide a more reasonable reference for the dense urban agglomeration planning and construction, the research area is divided into urban area, Two levels outside the urban area, were divided into traffic area. The most suitable entropy model is used to predict the passenger flow distribution. Considering that the spatial distance has a heavy impact on the distribution of passenger flow in dense urban agglomerations, drawing on the idea of gravity distribution model, the generalized trip cost is introduced to reflect the traffic impedance and the maximum entropy model And revised respectively; and constructed two-layer maximum entropy model from two levels of urban area and urban area respectively. The double-layer maximum entropy model is used to predict the passenger flow distribution in Dongguan in 2020. The results show that the double-layer maximum entropy model can reduce the negative impact of over-large spatial extent, while the double-layer maximum entropy model is relatively independent, which can be mutually complementary and more suitable for the prediction of passenger flow distribution in dense urban agglomerations.