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首页> 外文期刊>Transportation planning and technology >Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework
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Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework

机译：基于活动的旅行需求预测中的微观模拟误差调查：以FEATHERS框架为例

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摘要

Activity-based models of travel demand have received considerable attention in transportation planning and forecasting in recent years. However, in most cases they use a micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a consequence, running a transport micro-simulation model several times with the same input will generate different outputs, which baffles practitioners in applying such a model and in interpreting the results. A common approach is therefore to run the model multiple times and to use the average value of the results. The question then becomes: what is the minimum number of model runs required to reach a stable result? In this paper, systematic experiments are carried out using Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS (FEATHERS), an activity-based micro-simulation modelling framework currently implemented for the Flanders region of Belgium. Six levels of geographic detail are taken into account. Three travel indices - average daily activities per person, average daily trips per person and average daily distance travelled per person, as well as their corresponding segmentations - are calculated by running the model 100 times. The results show that the more disaggregated the level, the larger the number of model runs is needed to ensure confidence. Furthermore, based on the time-dependent origin-destination table derived from the model output, traffic assignment is performed by loading it onto the Flemish road network, and the total vehicle kilometres travelled in the whole Flanders are subsequently computed. The stable results at the Flanders level provides model users with confidence that application of FEATHERS at an aggregated level requires only limited model runs.
机译：近年来，基于活动的出行需求模型在交通运输计划和预测中受到了广泛的关注。但是，在大多数情况下，它们使用微观模拟方法，因此不可避免地包括由随机分量的统计分布引起的随机误差。结果，用相同的输入多次运行运输微仿真模型将产生不同的输出，这使从业人员难以应用这种模型并解释结果。因此，一种常见的方法是多次运行模型并使用结果的平均值。问题就变成了：达到稳定结果所需的最少模型运行次数是多少？在本文中，使用预测家庭的进化活动-旅行及其环境影响（FEATHERS）进行了系统的实验，FEATHERS是目前在比利时佛兰德地区实施的基于活动的微观模拟建模框架。考虑了六个级别的地理详细信息。通过运行该模型100次，可以计算出三个旅行指数-每个人的平均每日活动，每个人的平均每日旅行和每个人的平均每日旅行距离及其相应的细分。结果表明，层次越细分，就需要更多模型运行次数来确保可信度。此外，基于从模型输出得出的时间相关的起点-目的地表，通过将交通荷载加载到佛兰德道路网中来执行交通分配，随后计算出整个佛兰德的总行驶公里数。 Flanders级别的稳定结果使模型用户可以放心，以汇总级别应用FEATHERS仅需要有限的模型运行。

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