论文标题
基于活动模型的贝叶斯校准
Bayesian Calibration for Activity Based Models
论文作者
论文摘要
我们考虑了基于活动的运输模拟器的校准和不确定性分析问题。基于活动的模型（ABM）依靠单个旅行者行为的统计建模来预测大都市地区的高阶旅行模式。输入参数通常是使用最大似然从旅行者调查中估算的。我们开发了一种使用高斯工艺模拟器使用流量流数据来校准这些参数的方法。我们的方法扩展了传统的模拟器来处理运输模拟器的高维和非平稳性。我们介绍了一个深度学习降低的降低模型，该模型与高斯过程模型共同估计以近似模拟器。我们使用几个模拟示例以及校准伊利诺伊州布卢明顿模型的关键参数来演示方法。
We consider the problem of calibration and uncertainty analysis for activity-based transportation simulators. Activity-Based Models (ABMs) rely on statistical modeling of individual travelers' behavior to predict higher-order travel patterns in metropolitan areas. Input parameters are typically estimated from traveler surveys using maximum likelihood. We develop an approach that uses a Gaussian Process emulator to calibrate those parameters using traffic flow data. Our approach extends traditional emulators to handle the high-dimensional and non-stationary nature of transportation simulators. We introduce a deep learning dimensionality reduction model that is jointly estimated with Gaussin Process model to approximate the simulator. We demonstrate the methodology using several simulated examples as well as by calibrating key parameters of the Bloomington, Illinois model.
