Abstract
To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.
Highlights
In recent years, China’s economy has developed rapidly, and the process of urbanization has gradually accelerated.e country has continuously increased its efforts to build public transportation
E research purpose of this study is to forecast shortterm passenger flow of a metro station. e main contributions and novelty of this paper are as follows: (1) In order to supplement the lack of scientific analysis of short-term metro passenger flow prediction problem, we formally describe the problem based on the data-driven model and analyze the difficulties of the problem to better describe the complexity of short-term metro passenger flow prediction
(4) In order to solve the problems that the existing methods cannot reflect the uncertainty of shortterm passenger flow and the prediction accuracy is not high enough, we use the integrated learning algorithm LightGBM as a prediction model to describe the nonlinear characteristics of short-term passenger flow and improve the prediction accuracy
Summary
China’s economy has developed rapidly, and the process of urbanization has gradually accelerated. E first city, which used this method for traffic prediction in 1962, was Chicago It is very suitable for the long-term prediction of passenger flow and is of great significance for the planning of rail transit networks, the construction of engineering. LightGBM is a new boosting framework model that was proposed by Microsoft in 2015 [9] It has a fast training speed, low memory consumption, can process massive data quickly, and has better model accuracy, which are suitable for solving the short-term passenger flow forecast problem of rail transit. (4) In order to solve the problems that the existing methods cannot reflect the uncertainty of shortterm passenger flow and the prediction accuracy is not high enough, we use the integrated learning algorithm LightGBM as a prediction model to describe the nonlinear characteristics of short-term passenger flow and improve the prediction accuracy. (5) e experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains a higher prediction accuracy than SARIMA, SVR, and BP network
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