Studies have shown that sociodemographic attributes significantly influence individuals' transportation choices. However, not all travel demand models do not account for this effect when predicting future travel scenarios. On the other hand, current integrated urban models (IUMs) that incorporate demographic dynamics mostly rely on conventional logit models and rule-based models. These models may not be optimal for complex modeling since they do not fully capture the non-linear relationship between inputs and output. In this research, we explore the feasibility of utilizing machine learning (ML) models to enhance the prediction of demographic dynamics within our proposed IUM—known as ‘STELARS’, in conjunction with conventional logit models. To address the challenge of the black-box nature of ML, we employ an explainable AI technique (xAI) to gain insights into the influence of the factors and compare them with the interpretation revealed by the logit models. Three demographic components are considered: marriage/common-law formation, separation and divorce, and childbirth events, while other components were developed using rate-based models. The results (on the testing dataset) indicate that ML models outperform conventional logit models in terms of overall accuracy by a margin of up-to 3%. However, when considering the true positive accuracy (correctly predicting the event of interest), a significant improvement of 30–48% is observed. Additionally, the xAI analysis reveals consistent interpretation with the logit model. Subsequently, we implemented our demographic dynamics module within our integrated urban modeling system to predict population changes in the Okanagan region of Canada. The multi-year validation of the simulation results against Census data suggests a reasonably close prediction of the observed population. We also optimize the runtime of the demographic dynamics module using vectorization, reducing the simulation time for the demographic changes in our study area (comprising approximately 200,000 individuals living in 85,000 households) to just about 100 s for the total 10 years of simulation. The development and implementation of this advanced demographic dynamics module to accurately predict the life events of individuals adds a fundamental capacity to the STELARS to be built as an event-based microsimulation model.

研究表明，社会人口特征显着影响个人的交通选择。然而，并非所有旅行需求模型在预测未来旅行场景时都没有考虑到这种影响。另一方面，当前包含人口动态的综合城市模型（IUM）主要依赖于传统的 Logit 模型和基于规则的模型。这些模型可能不是复杂建模的最佳模型，因为它们没有完全捕获输入和输出之间的非线性关系。在这项研究中，我们探索了利用机器学习 (ML) 模型结合传统 Logit 模型来增强我们提出的 IUM（称为“STELARS”）中人口动态预测的可行性。为了解决机器学习黑盒性质的挑战，我们采用可解释的人工智能技术 (xAI) 来深入了解因素的影响，并将其与 Logit 模型揭示的解释进行比较。考虑了三个人口组成部分：婚姻/同居关系形成、分居和离婚以及生育事件，而其他组成部分是使用基于比率的模型开发的。结果（在测试数据集上）表明，ML 模型在整体准确率方面优于传统 Logit 模型，最高可高出 3%。然而，当考虑真阳性准确性（正确预测感兴趣的事件）时，观察到显着提高了 30-48%。此外，xAI 分析揭示了与 Logit 模型一致的解释。随后，我们在综合城市建模系统中实施了人口动态模块，以预测加拿大奥肯那根地区的人口变化。根据人口普查数据对模拟结果进行的多年验证表明，对观察到的人口的预测相当接近。我们还使用矢量化优化了人口动态模块的运行时间，将我们研究区域（包括居住在 85,000 个家庭中的约 200,000 人）的人口变化的模拟时间减少到 10 年模拟的大约 100 秒。这种先进的人口动态模块的开发和实施可以准确预测个人的生活事件，为 STELARS 增加了基本能力，使其成为基于事件的微观模拟模型。