Lane-changing is a routine but difficult driving task with important implications on traffic flow characteristics. Despite significant progresses in lane-changing decision modeling, lane-changing models are often improperly calibrated due to two issues related to trajectory data processing. First, the time of insertion (i.e., the time instant where a vehicle crossed the lane marking) is incorrectly considered as the lane-changing decision point, since the lane-changing decision is typically made earlier. Secondly, there is an imbalance between the number of non-lane-changing and lane-changing events, where non-lane-changing events typically dominate trajectory data. These issues can overestimate model performance and biased parameters. In this paper, we propose a method that combines (i) the wavelet transform method to pinpoint the correct lane-changing decision point, and (ii) a case–control design to systematically neutralize the dominance of non-lane-changing events in the data. The proposed method is applied to two NGSIM datasets to assess the performance of four representative lane-changing models. Results uncover that (i) lane-changing models are sensitive to various degrees of data imbalance, (ii) regardless of a driver’s decision time window (e.g., 1 s, 2 s, or 3 s), an analysis time window of 6 s will work reasonably well for evaluating the performance of a lane-changing model, while the optimal control-to-case ratio is 1:1; and (iii) when possible, a temporal discretization interval (i.e., an approximation of a driver’s typical decision time window) of 2 s should be preferred, while 3 s should be avoided. The proposed method also enabled us to outline a performance range for the selected lane-changing models.

变道是一项例行但困难的驾驶任务，对交通流特性具有重要影响。尽管在换道决策建模方面取得了重大进展，但由于与轨迹数据处理相关的两个问题，换道模型通常无法正确校准。首先，插入时间（即车辆越过车道标记的时刻）被错误地视为换道决策点，因为换道决策通常较早做出。其次，非变道事件和变道事件的数量之间存在不平衡，其中非变道事件通常主导轨迹数据。这些问题可能会高估模型性能和有偏差的参数。在本文中，我们提出了一种方法，它结合了 (i) 小波变换方法来精确定位正确的换道决策点，以及 (ii) 案例控制设计来系统地抵消数据中非换道事件的主导地位。所提出的方法应用于两个 NGSIM 数据集，以评估四个具有代表性的变道模型的性能。结果表明 (i) 变道模型对不同程度的数据不平衡很敏感，(ii) 无论驾驶员的决策时间窗口（例如 1 秒、2 秒或 3 秒）如何，分析时间窗口为 6 秒对于评估换道模型的性能会相当有效，而最佳控制与案例比率为 1:1；(iii) 在可能的情况下，应该首选 2 秒的时间离散化间隔（即，驾驶员典型决策时间窗口的近似值），而 3 s 应该避免。所提出的方法还使我们能够概述所选变道模型的性能范围。