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Sample size needed for calibrating trip distribution and behavior of the gravity model

机译:校准行程分布和重力模型行为所需的样本量

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摘要

Conventional calibration algorithms of trip distribution models assume that the analyst has a whole base year trip matrix. To attain a whole trip matrix, the sample size for travel surveys needed to be as large as possible. However, this could be very expensive especially in large cities. Some studies in the past showed a small sized sample would be enough to estimate functional parameters of observed trip length frequency distribution. But the performance of a gravity model with small sized samples has never been addressed. This empirical study has shown that sample sizes as small as 1000 (even smaller for quick response studies) could be as dependable as large sample surveys using a line search calibration algorithm.
机译:行程分布模型的常规校准算法假定分析人员具有整个基准年行程矩阵。为了获得整个旅行矩阵,旅行调查的样本量必须尽可能大。但是,这可能会非常昂贵,尤其是在大城市中。过去的一些研究表明,较小的样本足以估计观察到的行程长度频率分布的功能参数。但是,小样本重力模型的性能从未得到解决。这项经验研究表明,使用线搜索校准算法,小至1000的样本量(对于快速响应研究甚至更小)与大样本调查一样可靠。

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