A Road Condition Information Prediction Method Based on Improved Decision Tree Algorithm

Abstract

The invention discloses a road condition information prediction method based on an improved decision tree algorithm, comprising: determining and analyzing the attributes of the road connectivity based on the influencing factors of the road connectivity; collecting road data, preprocessing the data, calculating information entropy, and calculating various attributes The attribute entropy of each attribute is calculated based on the correlation function value of each attribute, and the weight value of each attribute is calculated based on the correlation function value of each attribute; the information gain of each attribute is calculated based on information entropy, attribute entropy of each attribute and weight value of each attribute , according to the magnitude of the information gain of each attribute, the decision tree is constructed, and the road conditions are predicted according to the decision tree. The present invention builds a decision tree by calculating the correlation function value of the attribute and the attribute weight value obtained by information entropy expansion operation, which can overcome the problem that the traditional ID3 algorithm tends to select elements with more possible values ​​as high-weight attributes. Use the constructed decision tree to predict the degree of road congestion improvement in the next period.

Description

technical field [0001] The invention relates to the technical field of decision tree algorithms and road traffic flow models, in particular to a method for predicting road condition information based on improved decision tree algorithms. Background technique [0002] With the advancement of global urbanization, the number of motor vehicles in cities is increasing year by year. As of the end of June 2016, the number of motor vehicles in Beijing has reached 5.44 million, ranking first in the country. For mega-cities like Beijing, Tokyo, and New York, this figure will continue to rise over time. A huge number of motor vehicles will not only cause traffic congestion, but also air pollution, energy waste and other problems, hindering urban development and reducing people's living standards. Among the world's 200 large cities (population greater than 800,000), Beijing's traffic congestion ranks 15th. Congested traffic conditions not only bring extra time consumption to people's...

AI Technical Summary

Problems solved by technology

In these two algorithms, the sensitivity calculation will derive the corresponding neural network according to the input attribute value and train it, which will greatly increase the complexity of the algorithm, and the algorithm efficiency is not high; the analysis using the joint density function is only applicable to discrete The data is not necessarily compatible with the data of the present invention

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