Short-Term Traffic Prediction of the Urban Road Network based on the Intelligent Transportation System

As an important part of the intelligent transportation system research, traffic prediction is the premise of realizing traffic guidance and can provide decision-making basis for traveler service and traffic management. To realize the macromanagement of the entire road network, it must be based on the traffic information of all road sections in the road network. In fact, short-term traffic information has certain characteristics such as real-time, high-dimensional, nonlinear, and nonstationary characteristics, but the traffic information of the same road section has stability and regularity in different periods, and the short-term traffic state has a self-similarity. This makes short-term traffic information predictable. The prediction is made by using the information of the road sections with detectors related to it, and the dynamic dissimilarity matrix is introduced to deal with the three parameters of flow, speed, and time occupancy at the same time. The quantitative relationship between the traffic information of the nondetector road segment and the known traffic information of other road segments, so as to realize the prediction of the traffic information of the nondetector road segment and obtain the complete traffic information of the regional road network. In addition, we use the actual data of the local road network in a certain area to verify the feasibility of the method.