AI 中文总结
本文针对交通微观模拟中“自动校准”实为错觉的问题，提出综合基准揭示该现象、提供统一标尺，明确算法无定制调优的基线，为校准研究提供推进工具。
AI 中文摘要
交通领域的研究者试图将校准方法应用于高速公路交通微观模拟，这是对传统手动校准耗时且主观、以及用于校准的数据日益普及的回应。本研究认为这种“自动”校准在很大程度上是一种错觉：这些方法背后隐藏着大量未量化的定制手动工作。这种错觉阻碍了科学进步的核心要素——针对共享标准的客观比较，进而妨碍了评估、破坏了可复现性并导致研究碎片化。为解决这一问题，本文提出了一个综合基准，旨在同时揭示高速公路微观模拟中的校准错觉并提供统一标尺。在一系列场景下的实验结果给出了算法在无定制调优情况下可达到的新基线，揭示了仍存在的研究缺口，并提供了推进校准累积科学的工具。额外实验为校准错误的来源提供了洞见，这些错误产生于大规模高速公路校准场景，相较于方法通常开发所用的简化设置而言。
英文摘要
The transportation community seeks to use calibration methods for highway traffic microsimulation. This is a response to the time-consuming and subjective nature of traditional manual calibration, as well as the growing prevalence of data for calibration. This work argues that this "automatic" calibration is largely an illusion. A significant - and unquantified - amount of bespoke manual work is hidden behind these methods. This illusion inhibits a core component of scientific advancement: objective comparison against a shared standard. This impedes evaluation, undermines reproducibility, and fragments research. To address this gap, this paper introduces a comprehensive benchmark designed to simultaneously expose the calibration illusion for highway microsimulation and provide a common ruler. The results across a range of scenarios present a new baseline for what the algorithms can achieve without bespoke tuning, revealing the research gap that remains and providing a tool to advance a cumulative science of calibration. Additional experiments provide insights into the source of calibration errors that arise in large-scale highway calibration relative to the simplified settings under which methods are commonly developed.
CommentsWorking Paper, 41 pages, 11 figures
