Time-of-day interval partition (TIP) at a signalized intersection is of great importance in traffic control. There are two shortcomings of the traditional clustering algorithms based on traditional distance definitions (such as Euclidean distance) of traffic flows. First, some continuous time intervals are usually divided into small segments. Second, 0 o’clock (24 o’clock) is usually selected as the breakpoint. It follows that the relationship between TIP and traffic signal control is neglected. To this end, a novel cyclic distance of traffic flows is defined, which can make the end of the last cycle (24 o’clock of the last day) and the beginning of the current cycle (0 o’clock of the current day) cluster into one group. Next, a cyclic weighted k-means method is proposed, with centroid initialization, cluster number selection, and breakpoint adjustment. Lastly, the proposed method is applied to a real intersection to evaluate the benefits of traffic signal control. The conclusion of the empirical study confirms the feasibility and effectiveness of the method.

In this study, we propose a novel model-free feature screening method for ultrahigh dimensional binary features of binary classification, called weighted mean squared deviation (WMSD). Compared to Chi-square statistic and mutual information, WMSD provides more opportunities to the binary features with probabilities near 0.5. In addition, the asymptotic properties of the proposed method are theoretically investigated under the assumption $logp=o(n)$ . The number of features is practically selected by a Pearson correlation coefficient method according to the property of power-law distribution. Lastly, an empirical study of Chinese text classification illustrates that the proposed method performs well when the dimension of selected features is relatively small.