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Planning in-home activities in the ADAPTS activity-based model: a joint model of activity type and duration

机译:在ADAPTS基于活动的模型中计划家庭活动:活动类型和持续时间的联合模型

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摘要

The role of in-home activities in the process of planning and scheduling of individuals' daily activities has been traditionally ignored because of two reasons: (ⅰ) in-home activities are not directly involved with trips; and (ⅱ) scarcity of data sources that provide required details on planning and scheduling of these activities. However, considering the interchangeable nature of out-of-home and in-home activities, and the significant effects that they have on each other, we argue that failing to incorporate in-home activities may result in overestimating frequency and duration of out-of-home activities, which may lead to inconsistent and unrealistic activity schedules. Recently, we have upgraded the ADAPTS activity-based model to account for planning and scheduling of both in-home and out-of-home activities. This paper aims to enhance the in-home activity planning module by modelling the type and duration of the in-home activities in a joint structure. To achieve this goal, using the American Time Use Survey data, we estimate joint discrete-continuous models, in which activity type and activity duration are estimated by a multinomial logit and a log-linear regression model, respectively. The joint structure of these two models is established using copula approach that captures the unobserved shared factors affecting the two activity attributes. The results indicate that the estimated joint models significantly outperform the independent models in terms of goodness-of-fit and prediction accuracy.
机译:传统上,人们忽略了家庭活动在个人日常活动的计划和安排过程中的作用,这有两个原因:(ⅰ)家庭活动不直接与旅行有关; (ⅱ)缺乏提供有关这些活动的计划和安排所需详细信息的数据源。但是,考虑到户外活动和家庭活动的可互换性,以及它们之间的相互影响,我们认为未能纳入家庭活动可能会导致高估户外活动的频率和持续时间家庭活动,这可能导致活动时间表不一致和不切实际。最近,我们已经升级了ADAPTS基于活动的模型,以考虑到计划和安排家庭内和家庭外活动。本文旨在通过对联合结构中的家庭活动的类型和持续时间进行建模来增强家庭活动计划模块。为了实现这一目标,我们使用美国时间使用调查数据估算联合离散连续模型,其中活动类型和活动持续时间分别由多项式logit和对数线性回归模型估算。这两个模型的联合结构是使用copula方法建立的,该方法捕获了影响两个活动属性的未观察到的共享因子。结果表明,在拟合优度和预测准确性方面,估计的联合模型明显优于独立模型。

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