Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model’s goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations.

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集水区的不同处理如何影响城市轨道交通站级需求建模？

直接需求建模是估算城市轨道交通站点需求和确定显着影响此类需求的因素的有用工具。直接需求模型的构建涉及流域面积的确定。虽然确定集水区的方法有很多，但这些方法的选择是非常随意的。不同的方法会导致不同的结果，它们对结果的影响尚不清楚。本文拟从与流域面积相关的三个方面来研究这一问题：流域面积的大小、重叠区域的处理方法以及是否对流域区域应用距离衰减函数。通过在每个站点周围绘制缓冲区来定义五个集水区，半径距离从300到1500米不等，间隔300米。测试了三种处理重叠区域的方法，即朴素法、泰森多边形和等分法。通过对外部集水区应用较低的权重来考虑距离衰减的影响。分析了美国五个城市的数据。流域内的建筑环境特征被提取为解释变量。每个车站的年平均工作日客流量用作响应变量。为了进一步分析回归模型对结果的影响，三种常用的模型，包括线性回归、对数线性回归和负二项式回归模型，用于检查哪种类型的集水区产生最高的拟合优度。我们发现理想的缓冲区大小因城市而异，不同的缓冲区大小对模型的拟合优度和预测精度没有很大影响。当汇水区域重叠严重时，将重叠区域除以重叠次数可以提高模型结果。距离衰减函数的应用几乎不能改善模型结果。三种模型的拟合优度相当，但对数线性回归模型的预测精度最高。本研究可为研究人员和规划人员在构建城市轨道交通站点直接需求模型时如何选择集水区提供有益的参考。我们发现理想的缓冲区大小因城市而异，不同的缓冲区大小对模型的拟合优度和预测精度没有很大影响。当汇水区域重叠严重时，将重叠区域除以重叠次数可以提高模型结果。距离衰减函数的应用几乎不能改善模型结果。三种模型的拟合优度相当，但对数线性回归模型的预测精度最高。本研究可为研究人员和规划人员在构建城市轨道交通站点直接需求模型时如何选择集水区提供有益的参考。我们发现理想的缓冲区大小因城市而异，不同的缓冲区大小对模型的拟合优度和预测精度没有很大影响。当汇水区域重叠严重时，将重叠区域除以重叠次数可以提高模型结果。距离衰减函数的应用几乎不能改善模型结果。三种模型的拟合优度相当，但对数线性回归模型的预测精度最高。本研究可为研究人员和规划人员在构建城市轨道交通站点直接需求模型时如何选择集水区提供有益的参考。当汇水区域重叠严重时，将重叠区域除以重叠次数可以提高模型结果。距离衰减函数的应用几乎不能改善模型结果。三种模型的拟合优度相当，但对数线性回归模型的预测精度最高。本研究可为研究人员和规划人员在构建城市轨道交通站点直接需求模型时如何选择集水区提供有益的参考。当汇水区域重叠严重时，将重叠区域除以重叠次数可以提高模型结果。距离衰减函数的应用几乎不能改善模型结果。三种模型的拟合优度相当，但对数线性回归模型的预测精度最高。本研究可为研究人员和规划人员在构建城市轨道交通站点直接需求模型时如何选择集水区提供有益的参考。虽然对数线性回归模型具有最高的预测精度。本研究可为研究人员和规划人员在构建城市轨道交通站点直接需求模型时如何选择集水区提供有益的参考。虽然对数线性回归模型具有最高的预测精度。本研究可为研究人员和规划人员在构建城市轨道交通站点直接需求模型时如何选择集水区提供有益的参考。