Origin-destination missing data estimation for freight transportation planning: a gravity model-based regression approach
Origin-destination missing data estimation for freight transportation planning: a gravity model-based regression approach
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DOI：
10.1080/03081060.2014.927665
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发表时间：
2014-01-01
影响因子：
1.6
通讯作者：
Aydin, Saniye Gizem
中科院分区：
文献类型：
--
作者：
Shen, Guoqiang;Aydin, Saniye Gizem
本文开发了一种对数线性回归方法来估计稀疏原点-目的地（O-D）矩阵中的缺失数据，假设采样或观察到的O-D行程遵循良好的重力模式。从1997年、2002年和2007年美国商品流量调查（CFS） O-D值和吨位矩阵的已知部分随机选择样本对该方法进行了测试，并在州一级使用2007年美国O-D吨位矩阵进行了验证。对2007年粮安定吨位矩阵的缺失数据也进行了估计，利用矩阵的所有已知条目获得了最佳截距和系数。该方法的概念可以从重力模型扩展到任何嵌入在已知稀疏O-D矩阵集合中的强数学模式，以估计其缺失的细胞。
This paper develops a log-linear regression approach to estimate missing data in a sparse origin-destination (O-D) matrix assuming the sampled or observed O-D trips follow a good gravity pattern. The approach is tested with randomly selected samples from the known portions of 1997, 2002, and 2007 US Commodity Flow Survey (CFS) O-D value and tonnage matrices and validated with 2007 US O-D tonnage matrix at the state level. The missing data are also estimated for the 2007 CFS tonnage matrix with the best intercept and coefficients obtained using all known entries of the matrix. The concept of the approach can be extended beyond the gravity model to any strong mathematical pattern embedded in the known set of a sparse O-D matrix to estimate its missing cells.