Deep Neural Network Modeling Method for Train Delay Forecasting of High Speed Railway

Abstract

The invention discloses a depth neural network model modeling method for high-speed railway train delay time prediction, Combined with the characteristics of the obvious interaction between adjacent trains and the time series and non-time series influencing factors of train delays, a deep neural network model including circulating neural network and all-connected neural network is proposed in thetechnical field of rail transit. In this model, the non-time-series factors of delay are input into the fully connected neural network, and the time-series factors are input into the cyclic neural network to learn the interaction relationship between adjacent trains by its feedback mechanism. In order to identify the influence of the interaction between trains on the train delay, the prediction accuracy is high and the practical application ability is good. The absolute error and relative error of the prediction are lower than the support vector regression model, the ordinary neural network model and the Markov model.

Description

technical field [0001] The invention belongs to the technical field of rail transit, and in particular relates to a deep neural network model modeling method for predicting the delay time of high-speed railway trains. Background technique [0002] Since my country's high-speed railway began operation on August 1, 2008, it has achieved rapid development in just ten years. By the end of 2017, the operating mileage of my country's high-speed railway has exceeded 25,000 kilometers, accounting for 66% of the world's operating mileage. %, and the proportion of EMUs has accounted for more than 60% of the total number of passenger trains. The operation of high-speed railways has improved the structure of the railway network, eliminated bottlenecks and conflicts in passenger and cargo transportation, and promoted the continuous updating of railway construction and technical equipment. [0003] In the case of train delays, dispatchers' empirical dispatching organization principles hav...

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Problems solved by technology

[0018] 2) None of the models has the concept of "time series", but there are two types of factors affecting train delays: time series and non-time series, and the time-dependent relationship between delay and time series variables cannot be well fitted

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