Combined estimation method for road junction dynamic steering proportion based on Bayes weighting

The invention discloses a combined estimation method for a road junction dynamic steering proportion based on Bayes weighting. According to the method, three sub algorithms of an improved Kalman filtering algorithm, an improved back-propagation neural network algorithm and a genetic algorithm are designed to solve the road junction dynamic steering proportion by utilizing road segment traffic detected by all inlet roads and outlet roads of road junctions, historical data are combined based on the road junction dynamic steering proportion, correction on historical and current estimation deviation is considered comprehensively, calibration is carried out by utilizing a Bayes formula and weight is updated dynamically, and obtained results through the three sub algorithms are weighted to obtain the dynamic steering proportion estimated by the combined method. Aiming at different traffic flow situations, the dynamic steering proportions estimated by existing methods all have advantages and disadvantages in the aspects of precision and efficiency, the combined estimation method can embody the advantages of all the methods on the whole, local oversize deviation is avoided, the combined estimation method has the advantages of being strong in adaptability, high in precision, good in stability and optimal in entirety, and can provide basic data supporting for signal control and other real-time traffic management and information service systems.