Application of geographically weighted regression to the direct forecasting of transit ridership at station-level
Application of geographically weighted regression to the direct forecasting of transit ridership at station-level
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DOI：
10.1016/j.apgeog.2012.01.005
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发表时间：
2012-05-01
影响因子：
4.9
通讯作者：
Gutierrez, Javier
中科院分区：
文献类型：
--
作者：
Daniel Cardozo, Osvaldo;Carlos Garcia-Palomares, Juan;Gutierrez, Javier
近年来，基于地理信息系统(GIS)和多元回归分析的车站级客流预测模型应运而生。这些模型根据车站的特点和它们所服务的地区来估计每个车站的乘客上车人数。这些模型与传统的四步模型相比具有相当大的优势，包括使用简单、结果易于解释、反应迅速和成本低。然而，这些模型通常采用传统的假设参数稳定的普通最小二乘(OLS)多元回归。这项研究提出了一个直接模型，使用地理加权回归(GWR)来预测马德里地铁站的乘车情况。在这里，比较了用OLS模型和GWR模型得到的结果。GWR模型的结果比传统模型更好地拟合。此外，GWR模型提供的关于弹性的空间变化及其统计意义的信息提供了更现实和有用的结果。(C)2012爱思唯尔有限公司。保留所有权利。
In recent years, station-level ridership forecasting models have been developed based on Geographic Information Systems (GIS) and multiple regression analysis. These models estimate the number of passengers boarding at each station as a function of the station characteristics and the areas that they serve. These models have considerable advantages over the traditional four-step model, including simplicity of use, easy interpretation of results, immediate response and low cost. Nevertheless, the models usually use traditional ordinary least squares (OLS) multiple regression, which assume parametric stability. This study proposes a direct model that uses geographically weighted regression (GWR) to forecast boarding at the Madrid Metro stations. Here, the results obtained using the OLS and GWR models are compared. The GWR model results in a better fit than the traditional one. In addition, the information supplied by the GWR model regarding the spatial variation of elasticities and their statistical significance provides more realistic and useful results. (C) 2012 Elsevier Ltd. All rights reserved.