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Factors influencing trip generation on metro system in Madrid (Spain)

机译：影响马德里（西班牙）地铁系统上出行的因素

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In the last few years, ordinary least squares (OLS) models have been the most used regression models for estimating trips in urban areas. However, these models present some limitations, whose the most relevant is that they ignore the geographic variations of relationships among variables. On the other hand, the explanatory power of trip generation models can be enhanced using geographically weighted regression (GWR), which is a local form of linear regression used to model spatially varying relationships.In this paper, factors affecting daily trips made by the Metro system of Madrid (Spain) are analysed through a GWR model. In order to evaluate the factors mainly influencing daily trip generation, a number of explanatory variables, including socio-economic characteristics of the population, land use, accessibility, and transportation system attributes, were considered. The analysis led to the identification of seven explanatory variables to be included in the model specification.The GWR results captured the spatial variation of the relationships among the variables across the study region. The research study attempted to identify the variable that most influenced trip generation for different parts of the city through a comparison of GWR results between various city zones.
机译：在过去几年中，普通最小二乘（OLS）模型已成为估计城市出行次数最常用的回归模型。但是，这些模型存在一些局限性，它们最相关的是它们忽略了变量之间关系的地理变化。另一方面，可以使用地理加权回归（GWR）来增强出行生成模型的解释力，GWR是一种线性回归的局部形式，用于对空间变化关系进行建模。通过GWR模型分析了马德里（西班牙）的体系。为了评估主要影响日常出行的因素，考虑了许多解释性变量，包括人口的社会经济特征，土地使用，可及性和运输系统属性。分析结果确定了要包含在模型规范中的七个解释变量.GWR结果捕获了整个研究区域中变量之间关系的空间变化。该研究试图通过比较各个城市区域之间的GWR结果来确定对城市不同地区旅行产生影响最大的变量。

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