	US 12,469,387 B2
	Traffic light timing control method and system
Chuanxiang Ma, Wuhan (CN); Wei Chen, Wuhan (CN); Yan Zhang, Wuhan (CN); Jiaqi Wan, Wuhan (CN); and Shiyi Gan, Wuhan (CN)
Assigned to Hubei University, Wuhan (CN)
Filed by Hubei University, Wuhan (CN)
Filed on May 10, 2024, as Appl. No. 18/660,244.
Claims priority of application No. 202310046437.8 (CN), filed on May 10, 2023.
Prior Publication US 2024/0378996 A1, Nov. 14, 2024
Int. Cl. G08G 1/08 (2006.01); G08G 1/01 (2006.01)

CPC G08G 1/08 (2013.01) [G08G 1/0129 (2013.01)]

9 Claims

1. A traffic light timing control method, comprising the following steps:

S1: completing monitoring of traffic flow in a specific road section, and recording and storing traffic flow data in each of N data collection gaps, to obtain a historical dataset comprising N traffic flow data samples, wherein traffic flow data in each data collection gap comprises: recording start time, traffic flow in this gap and crowd or non-crowd;

S2: dividing the historical dataset into a sample PA comprising M pieces of crowd data and a sample PB comprising (N−M) pieces of non-crowd data according to the crowd or non-crowd, and assigning a crowding level to each piece of traffic flow data in the sample PA and the sample PB;

S3: obtaining the duration of a green light of each piece of traffic flow data in the sample PA and the sample PB separately;

S4: generating a preliminary classification model A_iaccording to the duration of the green light;

S5: obtaining traffic flow data at the current moment, predicting the duration of a green light for traffic flow according to the preliminary classification model A_ior an optimized classification model A_i+1of the preliminary classification model A_i, and predicting the duration of a red light in traffic lights to which the green light belongs according to the predicted duration of the green light;

and S6: controlling timing of the green light and the red light on the same traffic lights according to the predicted duration of the green light and the red light;

wherein step S3 comprises:

S31: obtaining the optimal number A_kpointof clusters of the sample PA and the optimal number B_kpointof clusters of the sample PB separately;

S32: obtaining a cluster set C_A_₁, C_A_₂, . . . , C_A_{_kpoint}of the sample PA and a cluster set C_B_₁, C_B_₂, . . . , C_B_{_kpoint}of the sample PB separately;

S33: obtaining an average traffic flow value corresponding to each cluster in the cluster set C_A_₁, C_A_₂, . . . , C_A_{_kpoint}according to the equation (2-1), sorting the average traffic flow values, and obtaining an average traffic flow value corresponding to each cluster in the cluster set C_B_₁, C_B_₂, . . . , C_B_{_kpoint}according to equation (2-2), sorting the average traffic flow values;

wherein p_iis a traffic flow value in an i^thpiece of traffic flow data in a corresponding cluster, and n is the total number of traffic flow data in the corresponding cluster;

S34: assigning different durations of the green light for the traffic flow according to a sorting result of the average traffic flow values;

and S35: adding the crowding level and the duration of the green light to the historical dataset, to complete of updating of the historical dataset;

wherein step S4 comprises:

S41: using the traffic flow, the crowd or non-crowd and the crowding level in the updated historical dataset as training characteristics;

S42: calculating Gini values of each training characteristic under different division standards separately according to equation (4), and selecting the minimum Gini values and a corresponding division standard, to obtain the minimum Gini values a1, a2 and a3 corresponding to the traffic flow, the crowd or non-crowd, and the crowding level in the gap separately;

wherein m is a category set of a specific raining characteristic A, |m| is the number of elements in the set, m_nis the n^thelement in the set, |D_i| is the number of a specific category i with the training characteristic A, |D_m−i| is the total number of categories other than the category i with the training characteristic A, and |D| is the total number of categories with the training characteristic A;

S43: comparing the minimum Gini values a1, a2 and a3 corresponding to the traffic flow, the crowd or non-crowd and the crowding level separately in the gap to determine the global minimum Gini value min;

S44: determining the division standard corresponding to the global minimum Gini value min as a branch node of a current decision tree;

and S45: repeating the foregoing steps S42 to S44 to construct a multicategory decision tree, wherein the multicategory decision tree is intended to obtain the preliminary classification model A_i.