Dealing with uncertainty in agent-based models for short-term predictions
Dealing with uncertainty in agent-based models for short-term predictions
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DOI：
10.1098/rsos.191074
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发表时间：
2019-08
影响因子：
3.5
通讯作者：
L. Kieu;Nicolas Malleson;A. Heppenstall
中科院分区：
文献类型：
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作者：
L. Kieu;Nicolas Malleson;A. Heppenstall
基于主体的模型(ABM)作为社会科学中最强大的建模工具之一，正受到越来越多的关注。它们特别适合于模拟复杂的系统。尽管ABM在方法论上取得了许多进步，但其中一个主要缺点是它们无法结合实时数据来做出准确的短期预测。本文提出了一种允许动态优化ABM的方法。通过参数校准和数据同化(DA)的结合，提高了使用ABM进行实时模式预报的精度。我们以公交线路系统为例，对这些方法进行了探讨。本研究中开发的公交路线作业成本模型是可以通过参数校准和DA的组合动态优化的作业成本模型的例子。提出的模型和框架是一种新的可移植的方法，可以用于任何乘客信息系统，或用于智能交通系统，以提供公交车位置和到达时间的预测。
Agent-based models (ABMs) are gaining traction as one of the most powerful modelling tools within the social sciences. They are particularly suited to simulating complex systems. Despite many methodological advances within ABM, one of the major drawbacks is their inability to incorporate real-time data to make accurate short-term predictions. This paper presents an approach that allows ABMs to be dynamically optimized. Through a combination of parameter calibration and data assimilation (DA), the accuracy of model-based predictions using ABM in real time is increased. We use the exemplar of a bus route system to explore these methods. The bus route ABMs developed in this research are examples of ABMs that can be dynamically optimized by a combination of parameter calibration and DA. The proposed model and framework is a novel and transferable approach that can be used in any passenger information system, or in an intelligent transport systems to provide forecasts of bus locations and arrival times.