Estimation results of Thursday morning.

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors’ knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance.

带有空间起源-目的地（origin-destination, OD）过滤器的空间交互模型（spatial interaction models），是刻画空间出行流的有力工具，而空间出行流研究亦是区域科学领域的经典重要议题。据作者所知，现有采用OD过滤器的相关研究大多将空间相关性（spatial dependence）设定为自回归过程（autoregressive process），这或许未能完整呈现空间效应（spatial effects）的全貌。为探究该问题，本文提出三项假设：其一，空间交互模型中的空间OD相关性，可同时存在于空间自回归项（spatial autoregressive term）与空间误差项（spatial error term）中；其二，针对搭载OD过滤器的空间自回归与空间误差自回归（SARAR）模型进行估计，能够厘清空间相关性的存在位置与影响强度；其三，若从统计学视角来看，SARAR模型优于空间自回归（SAR）模型与空间误差（SEM）模型，则分析者将更倾向于采用SARAR模型得到的边际效应（marginal effects）。为验证上述假设，本文构建、估计并应用搭载OD过滤器的SARAR模型，以开展出行分布研究。本文通过与其他备选模型进行对比，采用中国杭州采集的实证数据，对SAR、SEM与SARAR三类模型的估计结果展开分析。本文的研究贡献在于，首次构建了适用于出行分布分析的搭载OD过滤器的SARAR模型，并对其模型性能进行了检验。