A Dynamic Prediction Model of Real-Time Link Travel Time Based on Traffic Big Data

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In order to improve the dynamic prediction ability of the real-time segment travel time in the traffic information platform, traffic big data can effectively feedback traffic congestion. A real-time link travel time dynamic prediction algorithm based on big data analysis is proposed. The structure model of interactive traffic information platform is constructed by using Small-World model, and the traffic state set of traffic information platform is sampled by using RFID tag reading technology. The real-time traffic condition big data in the sampled traffic information platform is processed by information fusion, and the principal component characteristic quantity of the real-time traffic condition big data in the traffic information platform is extracted, and the travel time and road network state information of the real-time road section are reorganized. According to the main component feature extraction of traffic big data in the traffic information platform, the real-time road condition monitoring and travel time prediction are carried out, and the basis of traffic big data analysis, real-time dynamic prediction of road travel time was carried out on the traffic information platform. The simulation results show that the proposed method is more accurate, and the anti-congestion and traffic capacity of the traffic network is improved by using the method to predict the dynamic travel time of the real-time section of the traffic information platform.