A real-time passenger flow forecasting method and a forecasting system based on rail transit

Abstract

The invention discloses a real-time passenger flow forecasting method and a forecasting system based on rail transit. Firstly, historical passenger flow data of four different periods of ten-day, week, day and hour are obtained. The historical passenger flow data of four different periods are preprocessed to obtain the time series set. Secondly, the ten-day time model, week time model, day time model and hour time model are established according to the time series set. Ten-day-time model, week-time model, day-time model and hour-time model are used to forecast passenger flow, and the first forecast data set, the second forecast data set, the third forecast data set and the fourth forecast data set are obtained respectively. Then, an interactive multi-model filtering algorithm is used to determine a passenger flow prediction model according to the first prediction data set, the second prediction data set, the third prediction data set and the fourth prediction data set. Finally, the real-time passenger flow of rail transit is forecasted according to the passenger flow forecasting model, which improves the accuracy of real-time passenger flow forecasting based on rail transit.

Description

technical field [0001] The invention relates to the field of rail transit passenger flow prediction, in particular to a prediction method and a prediction system based on rail transit real-time passenger flow. Background technique [0002] With the rapid development of urbanization, the conflict between the travel demand of the urban population and the urban traffic carrying capacity has become more and more prominent. With its unique advantages of high speed, high capacity and environmental protection, urban rail transit stands out from various transportation modes and becomes the main means of transportation to solve traffic congestion. Cities have invested in construction one after another, transforming urban rail transit from single-line operation to line-network operation. While increasing its scale and complexity, it also poses challenges to the network management and development of rail transit. Fast and accurate passenger flow forecasting is not only the basis for s...

AI Technical Summary

Problems solved by technology

[0003] Existing rail transit real-time passenger flow prediction methods include a dynamic Bayesian network method proposed by Roos J to predict short-term passenger flow in the Paris railway network. The problem of low prediction accuracy

Also including the artificial neural network method used in passenger flow forecasting has also achieved good results, but there is also the problem of low forecasting accuracy, so how to improve the accuracy of forecasting based on rail transit real-time passenger flow has become a technical problem that needs to be solved urgently in this field

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