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使用带有空间自回归干扰的空间自回归模型来研究原点-目的地旅行流
1Logistic School, Beijing Wuzi University, Beijing, China.
PloS one
|June 26, 2024
概括
这项研究引入了一种新的空间模型,以更好地了解旅行流. 带有起源-目的地 (OD) 过器的空间自回归模型与空间自回归干扰 (SARAR) 模型揭示了旅行分布中真正存在的空间依赖.
科学领域:
- 区域科学 地区科学
- 空间分析 空间分析
- 运输 地理 交通 地理
背景情况:
- 带有起源-目的地 (OD) 过器的空间交互模型对于分析行程流量至关重要.
- 现有的模型往往过于简化了空间依赖性,主要使用自回归过程.
- 旅行分布中的空间效应的全部程度仍然不完全理解.
研究的目的:
- 在空间交互模型中调查空间依赖的存在和程度,无论是在自回归还是错误方面.
- 引入和评估一个带有空间自回归干扰 (SARAR) 模型的空间自回归模型,其中包含了OD过器.
- 确定SARAR模型是否提供优越的统计性能和比传统SAR和SEM模型更有洞察力的边际效应,用于行程分布分析.
主要方法:
- 使用OD过器的SARAR模型的规范和估计.
- 对SARAR模型与空间自回归 (SAR) 和空间错误模型 (SEM) 的比较分析.
- 模型应用于来自中国杭州的实证旅行分布数据.
主要成果:
- 使用OD过器的SARAR模型成功地解开了旅行流中的空间依赖的位置和大小.
- 统计比较表明,SARAR模型在特定情况下可以超过SAR和SEM模型.
- 来自SARAR模型的边际效应提供了对行程分布驱动因素的更细致的理解.
结论:
- 使用OD过器开发的SARAR模型代表了旅行分布分析的新方法.
- 这种方法提供了一个更全面的特征旅行流的空间依赖关系.
- 这些发现支持SARAR模型对于区域科学和运输研究的有用性.
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