作为多模式公交的重要组成部分,地铁与地面公交的衔接换乘是城市客运交通一体化的关键环节。本文基于南京市多源数据分析地铁与公交之间的换乘需求,以地铁公交换乘量为因变量构建多尺度地理加权回归模型,揭示地铁站点周边共享单车使用...作为多模式公交的重要组成部分,地铁与地面公交的衔接换乘是城市客运交通一体化的关键环节。本文基于南京市多源数据分析地铁与公交之间的换乘需求,以地铁公交换乘量为因变量构建多尺度地理加权回归模型,揭示地铁站点周边共享单车使用量、公交供给特性、换乘可达性以及地铁网络特性对换乘需求的影响及其空间异质性。研究结果表明:多尺度地理加权回归模型相比于线性回归模型以及传统的地理加权回归模型具有更强的解释力,地铁公交换乘量的影响因素具有显著的空间异质性;公交运营班次供给以及可达站点数量的提升能够促进地铁公交间的换乘;公交站点周边住宅型POI(Point of Interest)数量在城市外围区域对换乘量起到促进作用,企业型POI数量则对换乘量起到抑制作用;共享单车借用量会抑制地铁与公交之间的换乘需求,特别是在与中心城区联系紧密的城市外围区域。展开更多
A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of avail...A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.展开更多
文摘作为多模式公交的重要组成部分,地铁与地面公交的衔接换乘是城市客运交通一体化的关键环节。本文基于南京市多源数据分析地铁与公交之间的换乘需求,以地铁公交换乘量为因变量构建多尺度地理加权回归模型,揭示地铁站点周边共享单车使用量、公交供给特性、换乘可达性以及地铁网络特性对换乘需求的影响及其空间异质性。研究结果表明:多尺度地理加权回归模型相比于线性回归模型以及传统的地理加权回归模型具有更强的解释力,地铁公交换乘量的影响因素具有显著的空间异质性;公交运营班次供给以及可达站点数量的提升能够促进地铁公交间的换乘;公交站点周边住宅型POI(Point of Interest)数量在城市外围区域对换乘量起到促进作用,企业型POI数量则对换乘量起到抑制作用;共享单车借用量会抑制地铁与公交之间的换乘需求,特别是在与中心城区联系紧密的城市外围区域。
基金Project(51561135003)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(51338003)supported by the Key Project of National Natural Science Foundation of China
文摘A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.
