In order to overcome the complexity of the single factor and multi-factor method of time-of-day control in the constant-peak-type intersections, this paper proposes a novel optimization model of the division of time-of-day control segmented points of constant-peak-type intersections based on sensor networks and data-driven. The dimension of traditional traffic flow data is increased by sensor networks, and a vector quantity is developed to represent the size, direction, and average time frequency with conflict point traffic of the total traffic flow at a certain intersection for a period by introducing a 3D vector of intersection traffic flow. A time-series segmentation algorithm is used to merge the distances between adjacent 3D vectors to obtain the time-of-day control scheme. The actual traffic flow data of a city in 2016 is used as the test data for comparative analysis. It is shown that when the innovated double-order optimization model is used in the intersection according to the constant-peak-type traffic flow characteristic, its control is more accurate and efficient than that of the traditional total flow segmentation model. The total delay time is reduced by approximately 6.04%.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1905-0184