Cyclic Weighted k -means Method with Application to Time-of-Day Interval Partition

Gaizhen Wang, Wei Qin and Yunhao Wang
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Gaizhen Wang: Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China
Wei Qin: School of Transportation, Jilin University, Changchun 130022, China
Yunhao Wang: Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China

Sustainability, 2021, vol. 13, issue 9, 1-13

Abstract: 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.

Keywords: cyclic data; cyclic distance; cyclic weighted k -means; time-of-day interval partition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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