在日常日程安排选择之间进行权衡

IF 2.8 3区 经济学 Q1 ECONOMICS Journal of Choice Modelling Pub Date : 2022-06-01 DOI:10.1016/j.jocm.2022.100354
Janody Pougala , Tim Hillel , Michel Bierlaire
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引用次数: 12

摘要

我们提出了一种新的日常活动调度建模方法,该方法将不同的日常调度选择维度(活动参与、地点、时间表、持续时间和交通方式)集成到一个单一的优化问题中。我们的方法背后的基本行为原则是,个人根据他们的个人需求、约束和偏好,安排他们的一天,以最大限度地提高他们从他们完成的活动中获得的总体效用。通过将多个选择组合到单个优化问题中,我们的框架能够捕获多个活动的调度决策之间的复杂权衡。这些权衡可能包括在一项活动上花费更长时间将如何减少其他活动的时间可用性,或者活动的顺序如何决定旅行时间。实现的框架将一组考虑过的活动、相关的地点和旅行模式作为输入,并使用这些来产生单个时间表的经验分布,从中可以得出不同的日常时间表。该模型使用瑞士移动和交通微观普查的历史旅行日记数据进行说明。结果表明,所提出的框架能够在严格的时间约束下为不同的活动生成复杂而真实的开始时间和持续时间分布。然后将生成的时间表与来自历史数据的总体分布进行比较,以演示我们的方法的可行性和灵活性。
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Capturing trade-offs between daily scheduling choices

We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The fundamental behavioural principle behind our approach is that individuals schedule their day to maximise their overall derived utility from the activities they complete, according to their individual needs, constraints, and preferences. By combining multiple choices into a single optimisation problem, our framework is able to capture the complex trade-offs between scheduling decisions for multiple activities. These trade-offs could include how spending longer in one activity will reduce the time-availability for other activities or how the order of activities determines the travel-times. The implemented framework takes as input a set of considered activities, with associated locations and travel modes, and uses these to produce empirical distributions of individual schedules from which different daily schedules can be drawn. The model is illustrated using historic trip diary data from the Swiss Mobility and Transport Microcensus. The results demonstrate the ability of the proposed framework to generate complex and realistic distributions of starting time and duration for different activities within the tight time constraints. The generated schedules are then compared to the aggregate distributions from the historical data to demonstrate the feasibility and flexibility of our approach.

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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
期刊最新文献
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