Simulating the effect of measurement errors on pedestrian destination choice model calibration

Accurately calibrated pedestrian destination choice models help explain and predict foot traffic in public places by describing how individuals choose locations to visit. Model calibration relies on empirical data, which is subject to measurement errors that can obfuscate calibration. This contribution adds errors to simulated data in a controlled and realistic way which can be applied to many model specifications, demonstrated on a pedestrian destination choice model. Results show that errors can cause calibrated models to generate dynamics that differ substantially from the true dynamics, along with causing bias in parameters and decreased prediction accuracy. By quantifying the size of errors and the impacts on calibration, this work aims to guide researchers in pedestrian destination choice modelling on what level of error is acceptable given the scope of their research.

经过精准校准的行人目的地选择模型（pedestrian destination choice models），可通过阐释个体选择到访场所的行为逻辑，实现对公共场所人流状况的解释与预测。模型校准依赖经验数据，而经验数据易受测量误差影响，此类误差可能干扰校准流程。本研究通过可控且贴合实际场景的方式为模拟数据引入误差，该方法可适配多种模型设定，并以行人目的地选择模型为例开展了验证工作。实验结果显示，测量误差会使校准后的模型生成与真实系统动态存在显著差异的运行动态，同时还会引发参数偏倚并降低预测精度。本研究通过量化误差规模及其对校准流程的影响，旨在为行人目的地选择建模领域的研究者提供参考，帮助其结合自身研究范围确定可接受的误差阈值。