Vehicle trajectories can offer the most precise and detailed depiction of traffic flow and serve as a critical component in traffic management and control applications. Various technologies have been applied to reconstruct vehicle trajectories from sparse fixed and mobile detection data. However, existing methods predominantly concentrate on single-lane scenarios and neglect lane-changing (LC) behaviors that occur across multiple lanes, which limit their applicability in practical traffic systems. To address this research gap, we propose a macro–micro approach for reconstructing complete vehicle trajectories on multi-lane freeways, wherein the macro traffic state information and micro driving models are integrated to overcome the restrictions imposed by lane boundary. Particularly, the macroscopic velocity contour maps are established for each lane to regulate the movement of vehicle platoons, meanwhile the velocity difference between adjacent lanes provide valuable criteria for guiding LC behaviors. Simultaneously, the car-following models are extended from micro perspective to supply lane-based candidate trajectories and define the plausible range for LC positions. Later, a two-stage trajectory fusion algorithm is proposed to jointly infer both the car-following and LC behaviors, in which the optimal LC positions is identified and candidate trajectories are adjusted according to their weights. The proposed framework was evaluated using NGSIM and HighD datasets, and the results indicated a remarkable enhancement in both the accuracy and smoothness of reconstructed trajectories, with performance indicators reduced by over 30% compared to two representative reconstruction methods. Furthermore, the reconstruction process effectively reproduced LC behaviors across contiguous lanes, adding to the framework's comprehensiveness and realism.

中文翻译：

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一种重建多车道高速公路上变道车辆轨迹的宏微观方法

车辆轨迹可以提供最精确、最详细的交通流描述，并作为交通管理和控制应用的关键组成部分。各种技术已被应用于从稀疏的固定和移动检测数据重建车辆轨迹。然而，现有的方法主要集中在单车道场景，而忽略了多车道上发生的换道（LC）行为，这限制了它们在实际交通系统中的适用性。为了解决这一研究空白，我们提出了一种在多车道高速公路上重建完整车辆轨迹的宏观-微观方法，其中宏观交通状态信息和微观驾驶模型相结合，以克服车道边界施加的限制。特别是，为每个车道建立宏观速度等值线图来调节车辆队列的运动，同时相邻车道之间的速度差为指导LC行为提供了有价值的标准。同时，跟车模型从微观角度进行了扩展，以提供基于车道的候选轨迹并定义 LC 位置的合理范围。随后，提出了一种两阶段轨迹融合算法来联合推断跟车和 LC 行为，其中识别最佳 LC 位置并根据权重调整候选轨迹。使用NGSIM和HighD数据集对所提出的框架进行了评估，结果表明重建轨迹的准确性和平滑度都有显着提高，与两种代表性重建方法相比，性能指标降低了30%以上。此外，重建过程有效地再现了相邻车道上的 LC 行为，增加了框架的全面性和真实性。