Abstract: The specific high complexity and nonlinear characteristics of large-scale road network traffic flow in a short period of time affect the accuracy of short-term traffic flow prediction. A short-term prediction method for large-scale road network traffic flow is studied based on improved neural network algorithms. Large-scale road network functions are constructed, road network functions are optimized by treating road sections as the core of the network and treating road nodes as corresponding connecting elements. Based on the optimized road network function, traffic flow features are extracted by combining the K-means algorithm with the EM ( Expectation-Maximization) algorithm. By combining genetic algorithm with Elman neural network algorithm, a short-term prediction of the traffic flow of the road network is carried out, and relevant prediction results are obtained.Experimental results have shown that the improved method's single point average speed prediction results are closer to the actual values, and the short-term prediction error of large-scale road network traffic flow is lower,resulting in higher reliability of the prediction results.

Key words: neural network algorithm, genetic algorithm, large scale road network, short term prediction of traffic flow, feature extraction, expectation-maximization(EM) algorithm