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Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation

机译：通过微仿扫描校准基于活动的旅行需求模型系统的配方和解决方法

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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.
机译：本研究解决了校准实用性最大化嵌套Logit活动的旅行需求模型系统的问题。在估计之后，通常使用聚合测量来校准在运输计划，政策制定和操作中校准估计的模型系统的参数。然而，基于活动的模型系统的校准受到更少的关注。现有的校准方法是近视启发式的意义，因为它们不考虑选择模型之间的基本依赖性，并且没有系统的方式来调整模型参数。此外，基于纯粹的仿真的方法在大规模应用中不得良好。在这项研究中，我们专注于实用程序 - 最大化嵌套的Logit活动的模型系统和校准聚合统计数据，例如活动共享，模式共享，时间相关和模式特定OD流，以及时间相关和模式特定的传感器计数。我们将校准问题作为基于仿真的优化问题，提出了一种基于随机梯度的解决方案方法来解决它。解决方法依赖于微疗，以计算校准问题的汇总统计数据的预期。另外，我们推导出对目标函数的梯度的近似分析表达式 - 在群体的微观批量上通过微染色来评估。所提出的解决方案程序对基于活动的模型系统的基本结构敏感，并且在考虑其模型组件的依赖项时是非近视的。配制的优化问题是非凸，高度非线性的，并且可能具有多个最小值。最后，我们展示了真实世界的应用 - 所提出的解决方案程序在计算效率，稳定性和收敛方面优于基于最先进的纯粹仿真的优化方法。我们还比较各种基于梯度的解决方案算法来确定更新参数的最佳算法。这项工作有可能促进更广泛，更容易应用基于活动的模型系统。

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