Crossroad dynamic turning proportion two-step prediction method based on double Bayes

The invention discloses a crossroad dynamic turning proportion two-step prediction method based on double Bayes. According to the crossroad dynamic turning proportion two-step prediction method, a first Bayes combination method is designed by means of link flow detected by entries and exits at a crossroad and with combination of history flow data to predicate entry and exist flow in the next time period, an improved Kalman filtering algorithm and an improved counterpropagation neural network algorithm are designed based on the entry and exist flow in the next time period to predicate dynamic turning proportion of the next first time period and the next second time period, a second Bayes combination method is designed under the conditions that predication errors are corrected through history turning proportion data to calibrate and update weight dynamically, and dynamic turning proportion one-step and two-step prediction values of double Bayes combination methods can be obtained. By means of the existing method, dynamic turning proportion one-step value can be obtained only, and the existing method has advantages and disadvantages in precise and efficiency. The crossroad dynamic turning proportion two-step prediction method comprehensively has the advantages of all methods, avoids local overhigh deviation, is high in precision, and can obtain one-step and two-step prediction values simultaneously and provide basis supporting for an intelligent traffic control system.