Utilising activity space concepts to sampling of alternatives for mode and destination choice modelling of discretionary activities

Utilising activity space concepts to sampling of alternatives for mode and destination choice modelling of discretionary activities
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利用活动空间概念对可自由支配活动的模式和目的地选择建模的替代方案进行抽样

DOI:
10.1016/j.jocm.2021.100336
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发表时间:
2022
影响因子:
2.4
通讯作者:
Tsoleridis P
Tsoleridis P
中科院分区:
经济学3区
文献类型:
--
作者:
Tsoleridis P

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在具有大量选择的数据集上估计的选择模型提出了导致计算成本迅速扩大的重大挑战,以及潜在的行为现实主义问题。选择抽样是克服计算限制的一种行之有效的方法,主要应用于住宅选址模型。尽管如此,自由裁量活动的目的地选择模型需要一种不同类型的分析,因为选择可能受到时空限制和对备选方案的熟悉程度的支配。使用高分辨率GPS跟踪观察几天的一般旅行区域可以提供个人行踪的重要信息。本研究利用这样一个数据集,提出了一个行为上更现实的采样协议,利用基于地理的活动空间概念来减少选择集。根据个体的活动空间和出行链,应用不同的重要抽样率,使最终的抽样选择集个人特定的空间意识和模式特定的时空约束的函数。利用全选择集估计模型评估了所开发的采样协议的性能,并与随机采样和其他几种重要采样协议进行了比较。建模结果表明,应该谨慎使用随机抽样,因为它可能导致高度偏倚的估计,但标准误差也很低。该方法结合了时空约束和个体空间意识,能够产生更少的偏差估计,实现更高的抽样稳定性和统计效率,同时也避免了过拟合。
Choice models estimated on datasets with large numbers of alternatives present significant challenges leading to rapidly expanding computational cost, as well as potential behavioural realism issues. Sampling of alternatives has been a well-established method for overcoming the computational limitations, mostly applied to models of residential location. Nonetheless, destination choice models of discretionary activities require a different type of analysis, since the choice can be governed by time–space constraints and familiarity regarding the alternatives. Observing the general areas of travel for a period of days using high resolution GPS tracking can provide important information of the individuals’ whereabouts. The present study, taking advantage of such a dataset, proposes a more behaviourally realistic sampling protocol to reduce the choice set utilising the geography-based concepts of activity spaces. Differential importance sampling rates are applied depending on the individual’s activity space and trip chain making the resulting sampled choice set a function of person-specific spatial awareness and mode-specific time–space constraints. The performance of the sampling protocol developed is assessed using a model estimated with the full choice set and compared with random sampling and several other importance sampling protocols. The modelling outputs suggest that random sampling should be used with care, since it can result in highly biased estimates, but with low standard errors, as well. The proposed approach incorporates both time–space constraints and individual spatial awareness and is able to produce less biased estimates, achieve higher sampling stability and statistical efficiency, while also avoiding overfitting.
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