Change point detection has always been one of the hot research topics in statistics. In actual data, there is usually a linear increase or decrease trend in a certain segment, the starting and ending point of this trend is unknown, thus, aiming at this kind of onedimensional data with piecewise linear trend, a multiple change point detection method is proposed. Based on the statistics constructed by the generalized loglikelihood ratio, this method combines the binary segmentation method, threshold criterion, and Strengthened Schwarz information criterion to quickly and effectively detect multiple change points in the data. Numerical simulation results show that the method is very accurate in detecting the position and number of change points for the data with piecewise linear trends, and the detection results are satisfactory. Finally, by taking the traffic flow data of Xinzhou Interchange of North Ring Avenue in Shenzhen as an example, the distribution characteristics of the change point of the area on working days and nonworking days are analyzed. The analysis results are consistent with the actual situation and can provide reference opinions for the relevant work of the traffic management departments.