基于城市轨道交通指标与城市特征的二维城市分类模型
何鸿杰陈先龙马小毅
Twodimensional Urban Classification Model Based on Urban Rail Transit Indicators and Urban Characteristics
HE HongjieCHEN XianlongMA Xiaoyi
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作者信息:广州市交通规划研究院有限公司， 510030， 广州
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Affiliation:Guangzhou Transport Planning Research Institute Co., Ltd., 510030, Guangzhou, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.08.005
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中图分类号/CLCN:TU984
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栏目/Col:学术专论
摘要:
目的：城市轨道交通项目建设成本高昂。未通车城市在轨道交通项目规划阶段无法判断未来轨道交通建设的合理性，需建立城市分类模型作为预测参照，以提供客流指标的参考值。方法：基于PCA(主成分分析)、ICA(因子分析)和层次聚类方法，建立包含城市总体特征分类子模型和城市轨道交通发展特征分类子模型的二维城市分类模型。通过城市总体特征分类子模型，使用已开通城市的社会经济发展数据和城市轨道交通运营数据，提取城市的总体特征，按年度对城市进行分类。通过城市轨道交通发展特征分类子模型，仅使用城市轨道交通运营数据，基于一段时期内的轨道交通发展趋势对城市进行分类。基于载荷矩阵和主因子方差贡献率选择因变量，并根据拟合优度选择自变量组合，利用组内指标均值，使用多元线性回归构建社会经济发展指标与城市轨道交通指标的关系。结果及结论：通过二维城市分类模型，可利用既有数据对已通车城市进行分类排序，赋予总体特征和发展特征，并建立重要城市轨道交通指标和社会经济发展指标关系。依据多元线性回归公式，从当前时点和未来发展两种角度能得到部分未通车城市的城市轨道交通指标预测值，以评价建设合理性。
Abstracts:
Objective: The construction cost of URT (urban rail transit) projects is high. At the planning stage of rail transit projects in cities where the system is not implemented, it is difficult to determine the feasibility of future rail transit construction. Therefore, it is necessary to establish an urban classification model as a reference for predicting passenger flow indicators.Method: Based on PCA (principal component analysis), ICA (independent component analysis), and Hierarchical Clustering, a 2D urban classification model is developed, comprising a general urban characteristic classification submodel and a classification submodel of URT development characteristics. The former extracts the overall characteristics of cities using socioeconomic development data and operational data from existing cities with rail transit system, classifying cities on an annual basis. The latter classifies cities based solely on URT operational data, considering the development trends of rail transit over a specific period. Load matrices and the contribution rates of principal factors are used to select dependent variables, and independent variable combinations are chosen based on goodness of fit. A multivariate linear regression is employed using the mean values of intragroup indicators to establish the relationship between socioeconomic development indicators and URT indicators.Result & Conclusion: The 2D urban classification model enables the classification and ranking of URT cities using available data, assigning them general characteristics and development features. It establishes the relationship between key URT indicators and socioeconomic development indicators. Based on the multivariate linear regression formula, predicted values of URT indicators for some cities where the system is not implemented can be obtained from both the current standpoint and future development perspective, facilitating the evaluation of construction feasibility.
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