为了提取城市路网中车辆实际的行驶轨迹，支撑交通规划、设计、管理和评价等需求，提出了基于梯度提升决策树的城市车辆丢失路径链的重构方法。首先，根据车牌号码匹配目标车辆，以时间排序提取视频检测器获得的路径链，并结合交叉口邻接矩阵及路段行程时间估计进行路径链初次分离; 然后，依据车辆出行特征和交通状况提取影响路径选择的关键特征，并基于此提出了基于梯度提升决策树的局部丢失路径链重构算法; 最后，以某市南明区实际视频车牌识别数据为例，根据重构算法准确性和计算效率验证了文中算法与传统算法。结果表明，本文算法的重构准确率达到 91%，对比传统算法，梯度提升决策树算法在车辆路径链重构方面有较大优势。

A reconstruction method for urban vehicle trip chain based on gradient boosting decision tree was pro-posed to extract actual vehicle trajectories for transportation planning，traffic design，management and evaluation．Firstly，vehicles were matched by license plate number (LPN)，and the corresponding travel chains sorted by time stamp were initially extracted and split according to the intersection adjacency matrix and estimated link travel time． Subsequently，the key features that affecting vehicle route choice were identified based on travel behavior a-nalysis and traffic conditions，and a reconstruction method for local lost trip chain was developed based on gradient boosting decision tree (GBDT)． Finally，taking the field LPN data from Nanming district of a Chinese city as an example，the accuracy and calculation efficiency of the proposed method and existing ones were verified． The re-sult shows that the proposed method can achieve a high reconstruction accuracy of 91%，and it superior to the tra-ditional ones in urban vehicle trip chain reconstruction．