Bus arrival time prediction method based on GRU neural network

Abstract

The invention discloses a bus arrival time prediction method based on a GRU neural network. The method comprises: outputting historical data from a database to a CSV format file, acquiring original data, and analyzing the original data by using an HBase distributed database and a Spark memory processing technology to remove hybridity, complexity and coefficient of the original data; on the basis of single attributes and a multi-factor perspective, analyzing and processing the processed original data by using a feature correlation research method to obtain standard time sequence type data; withthe Lasso method, carrying out variable selection on the standard time sequence type data, and rejecting a feature vector with weak correlation in the standard time sequence type data; and constructing a bus arrival prediction model based on a GRU neural network and inputting the standard time sequence type data with the feature vector with weak correlation rejected to the arrival prediction model, thereby realizing bus arrival time prediction operation. Therefore, the accuracy of the bus arrival time prediction is improved effectively.

Description

technical field [0001] The invention relates to the monitoring and arrival time prediction technology of urban public transport, in particular to a method for predicting the arrival time of public transport based on a GRU neural network. Background technique [0002] Public transportation is an important infrastructure related to the national economy and the people's livelihood. The development of an advanced information-based and intelligent public transportation system has positive significance for improving the management and service level of urban public transportation. Bus scheduling management is the core of an advanced bus system, and bus arrival time is a key parameter of bus dynamic scheduling management. Traditional bus scheduling is to simulate the arrival time based on the fixed travel time interval between stations judged by experience. . Usually, the estimated timetable produced by this method has large error and low degree of fitting, and cannot reflect the r...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the error of the bus arrival time prediction in the above-mentioned prior art is large and the fitting degree of its prediction cannot reflect the real situation of the bus arrival, the present invention proposes a method for predicting the bus arrival time based on the GRU neural network, The specific technical scheme is as follows:

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