AI 中文总结
本文提出含完整性控制合成设计等的结构化框架，针对QIDM和IDM2D两种随机跟驰模型，发现随机模型的确定性标定保证需重新检验，噪声项单独标定效果差，联合间距-速度拟合更优。
AI 中文摘要
标定随机跟驰模型比标定确定性跟驰模型更困难：损失本身成为随机变量，因此有利的随机实现可能被误认为是良好的参数向量。本文开发了一种用于标定随机跟驰模型的结构化框架，包括完整性控制的合成设计、修正的基于方差的敏感性分析（VBSA）以及最小实现（MRMIN）标定协议，应用于两种结构不同的随机机制QIDM和IDM2D。我们检验了确定性标定中的两个结论：一是少量参数以及轨迹本身在敏感性排序中占主导地位，二是一旦动力学具有随机性，间距标定仍会主导速度标定；并探究模型的噪声项是否可以单独标定。在平衡合成实验中，驾驶状态完整性的总效应指数均值与模型最具影响力的参数相当，且比配对身份约高两个数量级，这扩展而非反转了确定性研究中轨迹身份主导的结论。在MRMIN下，仅针对全种群确定性拟合标定噪声参数，会使1644条NGSIM轨迹的中位数间距误差翻倍以上；但先针对每条轨迹拟合确定性参数，再在此基础上标定噪声，可恢复原有性能。平均而言，间距标定仍比速度标定具有更强的跨维度鲁棒性，但确定性标定中该主导地位永不反转的保证，在两种机制下分别有19%-26%的轨迹被打破。相对误差空间中的多目标筛选则表明，联合间距-速度拟合优度函数优于单维度间距标定。因此，当模型为随机模型时，应重新检验确定性标定的保证，而非直接假定其成立。
英文摘要
Calibrating a stochastic car-following model is harder than its deterministic counterpart: the loss itself becomes a random variable, so a favorable random realization can be mistaken for a good parameter vector. This paper develops a structured framework for calibrating stochastic car-following models -- a completeness-controlled synthetic design, a corrected variance-based sensitivity analysis (VBSA), and the minimum-realization (MRMIN) calibration protocol -- across two structurally different stochastic mechanisms, QIDM and IDM2D. We test two claims from deterministic calibration -- that a small number of parameters, and the trajectory itself above all, dominates the sensitivity ranking, and that spacing calibration keeps dominating speed calibration once dynamics are stochastic -- and ask whether a model's noise term can be calibrated on its own. In a balanced synthetic experiment, driving-regime completeness has a mean total-effect index on par with the model's most influential parameter and roughly two orders of magnitude above pair identity, extending rather than reversing the deterministic finding on trajectory-identity dominance. Under MRMIN, calibrating only the noise parameter against a population-wide deterministic fit more than doubles median spacing error across 1644 NGSIM trajectories, but fitting the deterministic parameters per trajectory first and calibrating noise on top recovers it. Spacing calibration remains more cross-dimensionally robust than speed calibration on average, but the deterministic guarantee that this dominance can never reverse is violated in 19-26% of trajectories for both mechanisms. A multi-objective screen in relative-error space then favors joint spacing-speed goodness-of-fit functions over single-dimension spacing calibration. Deterministic calibration guarantees should therefore be re-tested, not assumed, once a model is stochastic.
