换道是一项关键的驾驶操作，理解其对交通安全和效率的影响对于有效的交通管理与优化至关重要。然而，现有研究在识别与换道相关的扰动时，难以充分考量自然交通动态。此外，衡量单次自由换道影响的空间范围与持续时间的方法，以及量化其整体时空影响的综合指标仍不完善。为弥补这些不足，本研究提出了一套综合解析框架，用于评估单次自由换道对上游交通的时空影响。该框架基于换道前后的车辆轨迹，识别受影响的跟驰车辆及其受影响时长。引入行驶距离偏差（Travel Distance Bias）指标以衡量跟驰车辆相对于局部参考车辆的运动偏差，同时利用车辆特定的事件前波动包络线和游程持续性滤波器，减少将背景交通波动误归因于换道事件的情况。基于受影响车辆及受影响区间，开发了两个聚合指标：总效率影响幅度（TEIM）和总安全影响幅度（TSIM），分别用于量化效率和安全相关的影响幅度。通过匹配无换道对照组和方法对比实验提供的比较证据表明，受影响车辆识别程序虽能减少但无法完全消除由背景交通波动引起的假阳性检测。

Lane-changing is a critical driving maneuver, and understanding its effects on traffic safety and efficiency is essential for effective traffic management and optimization. However, existing studies provide limited means to account for natural traffic dynamics when identifying disturbances associated with lane changes. Moreover, methods for measuring the spatial extent and duration of the impact of a single discretionary lane change, as well as comprehensive metrics for quantifying its overall spatiotemporal impact, remain underdeveloped. To address these gaps, this study proposes a comprehensive analytical framework to evaluate the spatiotemporal impact of a single discretionary lane change on upstream traffic. The framework identifies affected following vehicles and their impact durations from vehicle trajectories before and after the lane change. A Travel Distance Bias indicator is introduced to measure the motion deviation of following vehicles relative to local reference vehicles, while vehicle-specific pre-event fluctuation envelopes and run-length persistence filters are used to reduce the misattribution of background traffic fluctuations to the lane-change event. Based on the affected vehicles and affected intervals, two aggregate indicators, the Total Efficiency Impact Magnitude (TEIM) and the Total Safety Impact Magnitude (TSIM), are developed to quantify efficiency- and safety-related impact magnitudes. Matched no-lane-change controls and method-comparison experiments provide comparative evidence that the affected-vehicle identification procedure reduces, but does not eliminate, false-positive detections caused by background traffic fluctuations.