This study addresses the problem of calibrating utility-maximizing nested logit activity-based travel demand model-systems. After estimation, it is common practice to use aggregate measurements to calibrate the estimated model-system’s parameters prior to their application in transportation planning, policy making, and operations. However, calibration of activity-based model-systems has received much less attention. Existing calibration approaches are myopic heuristics in the sense that they do not consider the fundamental inter-dependencies among choice-models and do not have a systematic way to adjust model parameters. Also, other purely simulation-based approaches do not perform well in large-scale applications. In this study, we focus on utility-maximizing nested logit activity-based model-systems and calibrating aggregate statistics such as activity shares, mode shares, time-dependent & mode-specific OD flows, and time-dependent & mode-specific sensor counts. We formulate the calibration problem as a simulation-based optimization problem and propose a stochastic gradient-based solution procedure to solve it.

The solution procedure relies on microsimulation to calculate expectations of the aggregate statistics of interest to the calibration problem. Additionally, we derive approximate analytical expressions for the gradient of the objective function —that are evaluated through microsimulation on mini-batches of the population. The proposed solution procedure is sensitive to the fundamental structure of the activity-based model-system and is non-myopic in considering the dependencies across its model components. The formulated optimization problem is non-convex, highly nonlinear, and potentially has multiple-minima. Finally, we show —through a real-world application— that the proposed solution procedure outperforms other state-of-the-art purely simulation-based optimization approaches in terms of computational efficiency, stability, and convergence. We also compare various gradient-based solution algorithms to determine the best algorithm to update the parameters. This work has the potential to facilitate wider and easier application of activity-based model-systems.

这项研究解决了校准基于效用最大化的嵌套基于logit活动的旅行需求模型系统的问题。估算之后，通常的惯例是在将其应用于运输计划，政策制定和运营之前，使用汇总测量来校准估算的模型系统的参数。但是，基于活动的模型系统的校准受到的关注很少。现有的校准方法是近视启发式方法，因为它们没有考虑选择模型之间的基本相互依存关系，并且没有调整模型参数的系统方法。另外，其他基于纯模拟的方法在大规模应用中也不能很好地执行。在这个研究中，我们专注于使效用最大化的嵌套logit基于活动的模型系统，并校准汇总统计信息，例如活动份额，模式份额，与时间有关的和特定于模式的OD流量以及与时间有关的与特定模式有关的传感器计数。我们将校准问题公式化为基于仿真的优化问题，并提出了一种基于随机梯度的求解程序来解决该问题。

解决过程依赖于微观仿真来计算对校准问题感兴趣的总体统计数据的期望值。此外，我们导出了目标函数梯度的近似分析表达式，这些表达式是通过对人口的微型批次进行微观模拟来评估的。所提出的解决方案过程对基于活动的模型系统的基本结构很敏感，并且在考虑其模型组件之间的依赖关系时并非近视眼。制定的优化问题是非凸的，高度非线性的，并且可能具有多个最小值。最后，我们通过一个实际应用程序表明，在计算效率，稳定性和收敛性方面，所提出的解决方案性能优于其他最新的纯基于仿真的优化方法。我们还比较了各种基于梯度的求解算法，以确定更新参数的最佳算法。这项工作具有促进基于活动的模型系统更广泛，更容易应用的潜力。