Markov chain model based traffic factor network construction method

Abstract

The invention provides a Markov chain model based traffic factor network construction method. According to the method, captured speed and flow data serve as system factors, and a traffic factor network established via higher-order multi-component Markov chain simulates inherent complex space-time relation in the traffic network. Knowledge in the traffic field is applied to modeling, internal connection between traffic data is taken into consideration, and traffic factor values are clustered and analyzed. An EM algorithm based Gaussian mixture distribution model is used to learn historical datareversely, a lot of traffic flow parameter data is clustered and analyzed, and different traffic parameter clusters correspond to different traffic states in the practical traffic system, namely environment influential factor levels. The traffic factor state network is constructed by high order and multiple components on the basis, and thus, more accurate data prediction and correction can be realized.

Description

technical field [0001] The invention relates to the fields of machine learning and traffic big data analysis, specifically a method for constructing a traffic factor network based on a Markov Chain model, especially a new theoretical model for current traffic big data analysis. Background technique [0002] Among the many studies in the field of transportation, the prediction and estimation of traffic parameters, the correction of traffic data and the evolution of the entire traffic system are hot issues, and great progress has been made. But there are also some problems. For example, when analyzing traffic data, we often only consider the impact of single traffic parameters on the time series, and ignore the internal relationship between multiple traffic parameters. Another example is to generalize traffic data within a time period into the same traffic model, thereby ignoring different traffic models corresponding to different time periods or different combinations of traf...

AI Technical Summary

Problems solved by technology

However, there are still some problems. For example, when analyzing traffic data, we often only consider the impact of single traffic parameters on time series, and ignore the internal relationship between multiple traffic parameters.

Another example is to generalize the traffic data within a time period into the same traffic model, thus ignoring different traffic models corresponding to different time periods or different traffic parameter data combinations

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