AI 中文总结
研究印度司机是否符合纽厄尔跟车模型，用速度-间距关系回归和移动优化两种方法测试，发现交互边界效应是模型误差主因，边界校正能改善参数估计，确立其为基于轨迹校准纽厄尔型模型的关键步骤。
AI 中文摘要
纽厄尔简化的跟车模型以简洁性和行为可解释性著称，但有限交互边界对参数估计的影响仍了解不足，尤其是在无车道环境中。本研究用两种独立方法在印度无车道交通中测试该模型：速度-间距关系的聚合和特定对回归，以及通过最小化间距方差估计最优参数的移动优化。还提出移动方法的边界校正变体以隔离交互端点的过渡状态，同时保留原始纽厄尔公式。分析使用钦奈高速公路走廊的高分辨率车辆轨迹。特定对回归模型显著优于聚合规范，强调了驾驶员异质性的作用。轨迹移动恢复的参数分布在统计上等同于回归估计，独立支持轨迹平移原理。边界校正将平均轨迹拟合从平均$\bar{R^2}$=0.66提高到0.95，91%的对更喜欢校正后的公式。结果表明交互边界效应而非核心行为假设中的失败是此设置下模型误差的主要来源。校正这些效应可在不牺牲模型简洁性的情况下大幅改善参数估计。这些发现确立了边界校正是基于轨迹校准纽厄尔型模型的关键步骤，对交通状态估计和微观模拟有直接影响。
英文摘要
Newell's simplified car-following model offers behavioral interpretability with minimal parameters, yet the temporal limits within which it holds during actual car-following interactions, particularly in lane-free traffic, remain poorly understood. This has direct consequences for Newell parameter estimation. This study evaluates the model using high-resolution UAV trajectories from a lane-free highway corridor in Chennai, applying two independent estimation approaches: linear regression of the speed-spacing relationship (aggregate and pair specific), and a shifting optimization method that recovers parameters by minimizing spacing variance. A boundary-corrected variant of the shifting method is then introduced to isolate transitional regimes at interaction endpoints while preserving the original Newell formulation. Pair-specific regression substantially outperforms the aggregate specification ($R^2$ = 0.84 vs. 0.54), underscoring pronounced driver heterogeneity. Parameters recovered via trajectory shifting are statistically equivalent to regression estimates, providing independent support for the trajectory translation principle underlying Newell's model. Boundary correction markedly improves model fit, increasing the mean $\bar{R^2}$ from 0.66 to 0.95, and is preferred for 81% of pairs according to the Bayesian Information Criterion (BIC). The results indicate that interaction boundary effects, rather than failures of the core behavioral assumptions, are the principal source of estimation error in this setting. Correcting for these effects improves parameter recovery without compromising model parsimony. Boundary correction is therefore a necessary step in trajectory-based calibration of Newell-type models, with implications for traffic state estimation and microscopic traffic simulation.
