Statistical calibration of agent-based models of transport from aggregate data

本研究的重点是基于聚合数据的基于代理的模型（ABM）的运输统计校准。ABM旨在通过一组概率规则来理解交互的个体选择（例如路线旅行选择）对紧急行为（例如网络交通模式）的影响。ABM最近在流行病建模中越来越受欢迎，这提出了关于其可识别性和预测能力的重要问题，特别是在政策情景测试中（例如引入街道消毒）。该研究旨在开发计算统计和机器学习技术，通过从粗略观察的数据中理解离散的个体代理选择来解决这些问题。这带来了巨大的计算挑战，因为它需要探索的组合空间的代理决策。这个问题是在两个方面：人口合成和反弹道导弹模拟。前者是一个预处理步骤，创建人工代理，其社会经济属性具有相同的统计特性，人口平均值所描述的，而后者使用粗糙的数据来重建代理轨迹的空间-时间所产生的离散代理的选择和/或连续的决策参数。

This research focuses on statistical calibration of agent-based models (ABMs) of transport based on aggregate data. ABMs aim to understand the effect of interacting individual choices (e.g. route travel choices) on emergent behaviour (e.g. network traffic patterns) via a set of probabilistic rules. ABMs' recent rise in popularity in epidemic modelling raised important questions about their identifiability and predictive power especially in policy scenario testing (e.g. introduction of pedestrianised streets). The research aims develop computational statistics and machine learning techniques to address those questions by understanding discrete individual agent choices from coarsely observed data. This poses great computational challenges as it necessitates the exploration of a combinatorial space of agent decisions. The problem is tackled in two contexts: population synthesis and ABM simulation. The former is a pre-processing step that creates artificial agents whose socio-economic attributes have the same statistical properties as those described by population averages while the latter uses coarse data to reconstruct agent trajectories in space-time generated by discrete agent choices and/or continuous decision parameters.