Abstract
By analyzing and predicting the traffic states of urban road network, the formation of traffic congestion can be effectively alleviated, so as to improve the traffic capacity of urban road network. In this paper, firstly, we analyze and study the spatio-temporal correlation characteristics of traffic states based on the existing floating car data. At the same time, we extend the traffic conditions of urban road network from the upstream and downstream interaction to the global road network and complete the traffic congestion states discrimination of urban road network based on the spatio-temporal correlation. Secondly, according to the traffic jam aggregation and diffusion characteristics of local Moran's I, a mixed forest prediction method considering the spatio-temporal correlation characteristics of urban road traffic state is constructed by improving the existing random forest algorithm. Finally, an example is given to verify the effect of the prediction method on the short-term prediction of urban road network traffic states.
Highlights
At present, the road traffic congestion recognition algorithm was divided into two stages
Ning Z et al [4] built an intelligent system framework for vehicle edge computing based on deep reinforcement learning technology, established a communication and computing state model based on Finite Markov Chain, combined with two-way matching and deep reinforcement learning methods, jointly optimized task scheduling and network resource allocation strategies to maximize the quality of user experience (QoE)
There are two main factors that affect the accuracy of random forest algorithm: one is the tree association between any two decision trees in the forest: the stronger the independence between decision trees, the higher the accuracy of discrimination; the other is the accuracy of discrimination of a single tree: the stronger the accuracy of discrimination of a single decision tree, the lower the error rate of discrimination of the whole forest
Summary
The road traffic congestion recognition algorithm was divided into two stages. Based on the forward feedback neural network prediction algorithm proposed above, the road traffic flow state prediction model is constructed, and the traffic flow state prediction simulation is carried out by using the highway floating car data as the data input. The current research on traffic flow state prediction is analyzed It is mainly carried out by traditional methods such as BP neural network and support vector machine. Based on the Moran ’I, this paper introduces the time dimension, constructs the spatial-temporal Moran’ I, analyzes the spatiotemporal aggregation dissipative characteristics of the road network traffic flow, and combines the classification tree and the regression tree to construct a mixed forest prediction model based on the spatio-temporal state of traffic flow, and other traditions. The prediction method is compared with the error index of the Moran quadrant without adding the high correlation path as the eigenvector to prove the effect of the model prediction
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