A Time-of-the-day Partitioning Method for Traffic Signal Control Based on Key Intrinsic Mode Functions

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 ①Compares 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. ②The 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.