The use of Agent-Based and Activity-Based modeling in transportation is rising due to the capability of addressing complex applications such as disruptive trends (e.g., remote working and automation) or the design and assessment of disaggregated management strategies. Still, the broad adoption of large-scale disaggregate models is not materializing due to the inherently high complexity and computational needs. Activity-based models focused on behavioral theory, for example, may involve hundreds of parameters that need to be calibrated to match the detailed socio-economical characteristics of the population for any case study. This paper tackles this issue by proposing a novel Bayesian Optimization approach incorporating a surrogate model in the form of an improved Random Forest, designed to automate the calibration process of the behavioral parameters. The proposed method is tested on a case study for the city of Tallinn, Estonia, where the model to be calibrated consists of 477 behavioral parameters, using the SimMobility MT software. Satisfactory performance is achieved in the major indicators defined for the calibration process: the error for the overall number of trips is equal to 4% and the average error in the OD matrix is 15.92 vehicles per day.

翻译：基于智能体和基于活动的建模在交通领域中的应用日益增多，这得益于其应对复杂问题的能力，例如颠覆性趋势（如远程办公和自动化）以及细化管理策略的设计与评估。然而，由于固有的高复杂性和计算需求，大规模细化模型的广泛采用尚未实现。例如，侧重于行为理论的基于活动模型可能涉及数百个参数，这些参数需要针对每个案例研究进行校准，以匹配人口详细的社会经济特征。本文通过提出一种新颖的贝叶斯优化方法来解决这一问题，该方法结合了改进型随机森林形式的代理模型，旨在自动化行为参数的校准过程。所提出的方法在爱沙尼亚塔林市的案例研究中进行了测试，使用SimMobility MT软件对包含477个行为参数的模型进行校准。在校准过程中定义的主要指标上取得了令人满意的性能：总出行次数的误差为4%，OD矩阵的平均误差为每日15.92辆车。