为了在缺乏公交准确需求信息的情况下能够估算需求响应式公交（demand responsive transit, DRT）的“潜在需求”，以便在试运营前为线路规划提供可行性方案，提出了一种基于出租车轨迹数据的DRT线路规划方法。首先，通过数据挖掘手段获取研究区域内的出租车轨迹数据并进行预处理，分析轨迹数据的空间分布特征，将其视为该区域内乘客出行的“潜在需求”。其次，利用K均值（K-means）聚类算法确定备选站点，将备选站点连接形成基准站点网络，并将边缘基准站点设为线路的起点和终点。根据线路长度的约束条件，采用K条最短路径（K-shortest pathes, KSP）算法生成基准链条。最后，确定基准链条的子链条集合，并根据绕行临界值的约束条件搜索子链条的需求响应站点集合。结果表明：通过多次循环以上算法，可以生成时段内的所有备选线路，并根据各备选线路的综合评价指标选择该时段内初步的最优线路。同样的线路算法可确定不同时段内的最优线路初步方案。

In the absence of accurate transit demand information, a demand responsive transit(DRT) route planning method based on taxi trajectory data was proposed to predict the "potential demand" of demand responsive transit and provide a feasible plan for route planning before transit operation. Firstly, taxi trajectory data in the study area was obtained through data mining, representing the "potential demand" for passenger travel in the area, and candidate station were determined using the K-means clustering algorithm. Secondly, a benchmark station network was established using these candidate station, with edge benchmark stations designated as the starting and ending points of routes. Utilizing the K-shortest pathes(KSP) algorithm constrained by route length, benchmark chains were generated. Finally, after determining the sub-chain set of the benchmark chains, demand response stations within each sub-chain were searched based on circumferential critical value constraints. Using this algorithm, alternative routes were generated repeatedly within specific time periods, and an initial optimal route was selected based on comprehensive evaluation indices for each alternative route.