Research On Urban Freeway Traffic State Identification And Prediction

With the rapid growth of the social economy, the problem of urban traffic congestion has become increasingly acute. Reasonable analysis of the state of the road traffic plays an important role in coordination of traffic control systems and traffic flow guidance system. The identification of road traffic state, and predict the next time traffic status in a timely manner could induce travel traffic participants, so as to avoid traffic congestion sections, reducing travel time, thus alleviating road congestion; On the other hand, traffic managers can also real-time grasp the overall operation of the road, easy to make the right transportation decisions. Now traffic detection equipment have been installed on many city roads, gathering a wide range of traffic data, and how to make use of a large number of heterogeneous data to identify and forecast traffic state has certain research value.The expressway is a major part of the urban road network, undertaking the most amount of traffic travel in the city, and to a large extent the freeway traffic state reflects the traffic state of the urban road network and travel quality. In this paper, by using of a microwave sensor data, I make some research on freeway traffic state identification and forecasting methods. The main work is as followsFirstly analysis the way of acquisition and pre-processing on urban freeway traffic flow data,and elaborate the characteristic of traffic flow, thus provides theoretical support and data base for the identification and prediction of the traffic state.Secondly, on the basis of detailed analysis and summary of previous research on traffic state, the defined the concept of the traffic state, selected the discriminant index of state identification, used the improved fuzzy clustering method on traffic state discrimination, analyzed the transport parameters in the clustering feature weight, determine optimal class number in the traffic status clustering. Discrimination method was proposed in this study by using the actual data to test the improved method.Last Probit theory is used in the traffic state prediction, freeway traffic state prediction model was constructed based on the Ordered Probit regression. In order to verify the validity and accuracy of the model, statistical tests and model predictions to compare was used, and the results show that the model is accurate and the credibility is high.