Traffic signal control is an important tool to relieve urban traffic congestion and time-of-the-day partition is the basis for optimizing multi-period signal control at isolated signalized intersections in that a proper partition can significantly improve the efficiency of traffic control. For an intersection with a fixed-timing signal control strategy, traditional methods for time-of-the-day partition are usually based on experiences or simple clustering algorithms.These methods use historical traffic flow data to directly divide a day into several time periods, which fail to consider the stochasticity of traffic flow and the regularity of time sequence and lead to no contributions to the overall effectiveness of traffic control. To overcome this problem, this study proposes a new method for time-of-the-day partition,which uses an ensemble empirical mode decomposition（EEMD） and a fisher clustering algorithm. The intrinsic mode function（IMF） and corresponding residual from traffic flow data are extracted using EEMD. The Pearson correlation coefficient is calculated to analyze the relationship between the IMF, the residual, and the original traffic flow. The IMF or the residual that gives the highest correlation coefficient is identified as the key component, which replaces the traffic flows in the fisher clustering and partitioning process. The optimal number of clusters is determined by identifying the elbow point of the minimum loss values with different numbers of clusters, and the optimal time-of-the-day partition plan is obtained. A case study based on an intersection in the City of Zhongshan,Guangdong Province, is conducted to verify the proposed method. Simulations are carried out using the VISSIM software and study results show that（1） Compares to the current situation, the proposed method can increase the number of vehicles going through the intersections by about 11.32% and 2.62% and can reduce the queue length by about 18.67% and 12.02% on weekdays and weekends, respectively.（2）The proposed method also can reduce the average vehicle delay by 6.80% and the stopped delay by 5.87% at weekends, but cannot change both much during weekdays.

城市长距离交通干道中,小汽车和公交车的运行轨迹存在显著差异,合理的信号协调控制策略应统筹考虑两类车型的需求.针对包含大流量公交车的长距离干线绿波协调路口传统分组片面化的问题,分析了带有公交站台的长距离干道上小汽车和公交车时空轨迹特征,提出了差异化路口分组的信号协调方法.该方法将小汽车和公交车的协调路径分割点分别设置在路口和公交站点,构建了两类车型干道行驶的延误和等候时间最小化运筹学模型.实际路网的仿真证明:针对中山市中山路13个灯控路口,提出的长路径差异化分段协调较Multiband模型,其小汽车和公交车平均延误分别减少34.7％和13.7％,平均停车次数减少28.7％和30.0％.此外,针对饱和度、公共周期和公交停站时间3个关键指标的敏感性分析实验同样证明,提出的方法能同时为长距离走廊上的小汽车和公交车提供协调服务.