为提高单点交通控制时段划分准确性，首先提出一种基于KD-tree加速搜索并利用GridsearchCV进行超参数寻优的kagscv-DBSCAN算法用来求解基于检测器自然数据的时段划分初步方案，并加入了对方案间过渡的考虑对初步方案进行修正。最后，以车总延误、交叉口通行能力和运算速度为评价指标进行实际案例分析并与传统方法进行了对比。结果表明：所提方法对比K-means、PAW、FCM和RSAGA-FCM在车总延误方面分别降低8.69%、6.61%、3.68%和1.04%，通行能力提升效率分别为5.67%、5.07%、3.48%、1.69%，并在运算速度上具备明显优势。

In order to improve the precision of time interval partition at an isolated control intersection, a kagscv-DBSCAN algorithm based on KD-tree accelerated search and hyperparametric optimization using GridsearchCV was firstly proposed to solve the preliminary time of interval partition plan based on natural data of detectors, and was modified by adding the consideration of impacts of plan transition. Finally, the total vehicle delay, intersection capacity and computational speed were used as evaluation indexes for practical case analysis and comparison with traditional methods. The results show that the proposed method reduces the total vehicle delay by 8.69%, 6.61%, 3.68% and 1.04%, respectively, and improves the capacity by 5.67%, 5.07%, 3.48% and 1.69%, respectively, compared with K-means, PAW, FCM and RSAGA-FCM, and has a significant advantage in computing speed.