为提升动态时序网络分析的科学性和有效性,提出了一个系统化、数据驱动的方法选择时间尺度。首先引入多尺度熵理论描述客流时间序列的复杂性和随机性,确定合适的时间尺度选择范围。其次,从节点特性、边特性和整体结构3个方面选取网络特征指标,结合主成分分析和随机森林算法选择时间尺度。以3个网络为例进行验证,结果表明:该方法适用于不同交通系统的数据需求,具有较高的合理性和适用性。
To enhance the science and effectiveness of dynamic temporal networks analysis, a systematic, data-driven approach to selecting time scales is proposed. Firstly, the multi-scale entropy theory is introduced to describe the complexity and randomness of the passenger flow time series to determine the appropriate time scale selection range. Secondly, the network characteristic indexes are selected from node characteristics, edge characteristics and overall structure, and the time scale is selected by combining principal component analysis and random forest algorithm. Three networks are used as examples for validation, and the results show that the method is applicable to the data requirements of different transportation systems with high rationality and applicability.
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