Tunnel traffic accident duration prediction method based on PCA and Adaboost

Abstract

The invention discloses a tunnel traffic accident duration prediction method based on PCA and Adaboost, and the method comprises the following steps: importing historical traffic accident data: carrying out the preprocessing of the data, and dividing the data into a short grade, a medium grade, a long grade and an extra-long grade according to the duration of an accident; carrying out the missing value checking and processing on input variables in the prediction model; and finally, carrying out thermal coding processing on the classification variables. Herein, the PCA method is used for decentralizing original input variables and calculating a covariance matrix of the original input variables, and feature values and feature vectors of the original input variables are calculated on the basis, and a plurality of feature values and corresponding feature vectors are sequentially determined from small to large. Firstly, traffic accident duration is classified based on a weak classifier, and a basic classification result is obtained through sample training; and then, an Adaboost iteration framework is adopted to calculate classification error samples of the weak classifier, the weight of the classification error samples is improved, a next weak classifier is constructed on this basis, and a final strong classifier is obtained after multiple iterations.

Description

technical field [0001] The invention relates to the field of traffic accident prediction, in particular to a method for predicting the duration of expressway tunnels based on PCA (Principle Component Analysis, principal component analysis) and Adaboost (Adaboost classification). Background technique [0002] As the artery of national economy, expressway plays an incomparable role in long-distance transportation in cities. As a special structure in highway traffic, expressway tunnel is a frequent source of traffic accidents, and it is also a source of serious accidents. Compared with ordinary road sections, accidents in tunnel sections will cause a greater degree of traffic congestion, which will cause traffic travelers to spend more travel time and costs. series of social issues. Therefore, timely and accurate prediction of the duration of traffic accidents is a prerequisite for effective traffic control, and can provide a basis for the timely release of inductive and pred...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to solve the problem that the sample size of the tunnel traffic accident duration in the prior art is relatively large and the prediction accuracy is not high enough, and a method for predicting the duration of tunnel traffic accidents based on PCA and Adaboost is provided

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