An adaptive signal control optimization method based on E-police data was proposed by deeply mining and utilizing the traffic flow information contained in E-police data. The study object is a four-arm intersection where E-police detectors are deployed at each approach. Its four upstream intersections are also equipped with E-police detectors，which can obtain the traffic flow to the target intersection. Firstly，according to the matched historical data of upstream and downstream E-police in the TOD period，the link travel time parameters were estimated by Gaussian mixture model，and then the platoon discrete model based on truncated normal distribution was calibrated；Secondly，based on the predicted value of real-time arrival rate，taking the minimum total delay of the target intersection as the optimization objective，an integer programming model was established to optimize the signal control parameters in real-time by rolling horizon scheme，which was solved by dynamic programming；Finally，the proposed methods were simulated and verified in different demand scenarios. The results show that the estimation errors of the mean and standard deviation of link travel time are less than 3s，and the average vehicle delay and queue length are reduced by more than 34.2% and 40.5% respectively compared with the optimal group-based timing. Compared with the actuated control， the improvement of the proposed method is more obvious in high demand scenarios，where the average vehicle delay and queue length are reduced by 12.9% and 15.8% respectively， and more than 2.6% and 5.4% in other scenarios respectively. At the same time，the increase of planning horizon can also improve the control benefits.