基于乘客需求数据的定制商务班车站点选址方法

针对基础的聚类算法无法适应定制商务班车站点设置的问题，在传统的基于密度的带有噪声的空间聚类算法基础上，通过衡量类簇精细化服务指标确定分组效果，并对聚类效果不理想的组别依据其数据特征自动更新以扫描半径和最小包含点数为代表的聚类参数，进行迭代聚类，直到聚类效果达标为止。同时，结合节点重要度的思想改进基于密度的带有噪声的空间聚类算法，使其能够输出备选站点。研究结果表明，改进的算法能够较好地根据数据特征给出应有分组，给出的扫描半径和最小包含点参数能够较好地适应分组情况，备选节点能够有效地匹配周围的交通资源。

A site location method of customized business bus based on passenger demand data

Based on the traditional algorithm of density-based spatial clustering of applications with noise (DBSCAN), this paper determined the grouping effect by measuring cluster refinement service index. For groups with unsatisfactory clustering effect, the clustering parameters represented by scanning radius and minimum points were updated automatically according to their data characteristics, and then iterative clustering was carried out until the clustering effect was up to standard. At the same time, the DBSCAN algorithm was combined with the idea of node importance, which enabled it to output alternative sites. The results show that the improved DBSCAN algorithm can give the proper grouping according to the data characteristics, and the scanning radius and the minimum points parameters can be better adapted to the grouping situation, and the alternative nodes can effectively match the traffic resources around them.