Subway passenger congestion degree prediction method adopting a resampling recurrent neural network

Abstract

The invention relates to the field of traffic data analysis, in particular to a subway passenger congestion degree prediction method adopting a resampling recurrent neural network. The method comprises the following steps: 1, carrying out pretreatment; training sample data are set according to the original data; setting crowding degree label, dividing the sample data into n sub-sample sets according to the congestion degree labels, resampling the sub-sample sets to obtain resampling sequences, inputting the resampling sequences into a recurrent neural network model to train the recurrent neural network model, evaluating the recurrent neural network model, and adjusting resampling weights according to an evaluation result until the evaluation result is good. In the prior art, random sampling is often carried out from training sample data, but different types of samples are distributed unevenly, so that a recurrent neural network model overfits most samples and underfits few samples, andinaccurate prediction is caused. According to the method, secondary sampling is carried out on the samples through resampling, so that the model is fully trained, and the prediction precision is effectively improved.

Description

technical field [0001] The invention relates to the field of traffic data analysis, in particular to a subway passenger congestion prediction method using a resampling recursive neural network. Background technique [0002] With the development of technology, the subway has gradually become one of the main modes of travel for people because of its good convenience and fast transportation speed. As more and more passengers choose the subway as their main means of transportation, it has also intensified The degree of congestion during the peak period of the subway, and even the number of passengers during the peak period exceeds the utilization capacity of the subway, causing passenger congestion. Passenger congestion has seriously affected people's daily life and the development of urban transportation. Therefore, it is extremely important to release subway passenger congestion to the public in an effective and timely manner, and it is also one of the important means to solve...

AI Technical Summary

Problems solved by technology

However, in the training process, if the recurrent neural network model is directly randomly sampled from the sample data according to the conventional method, the model is easy to overfit most samples, making it difficult for the model to accurately predict the subway congestion.

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