Supplementary material from Dealing with uncertainty in agent-based models for short-term predictions

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.

基于智能体的模型（Agent-based models，ABMs）正逐渐成为社会科学领域内最具影响力的建模工具之一，尤其适用于复杂系统的仿真模拟。尽管基于智能体的模型在方法论层面已取得诸多进展，但其核心缺陷之一在于无法融入实时数据以开展精准的短期预测。本研究提出一种可实现基于智能体模型动态优化的方法：通过结合参数校准与数据同化（DA）技术，可提升基于智能体模型开展实时预测的准确性。本研究以公交路线系统为范例，对上述方法进行探究。本研究开发的公交路线基于智能体模型，正是可通过参数校准与数据同化结合实现动态优化的典型案例。本研究提出的模型与框架具备新颖性与可迁移性，可应用于各类旅客信息系统或智能交通系统中，以实现公交车辆位置与到站时间的预测。