Short-Term Passenger Flow Forecast in Urban Rail Transit Based on Enhanced K-Nearest Neighbor Approach

Bai, Jincheng; He, Mengchang; Shuai, Chunyan

doi:10.1061/9780784482292.148

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Zhang [29] combined principal component analysis (PCA) and error BP networks to predict bus passenger flow, which increases the convergence speed. Bai [30] used enhanced KNN methods by considering the trend factor and time interval factor of passenger flow, which gets a better performance than the BP network and original KNN method.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow

Han

Peng

Wang³

et al. 2021

IJGI

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Accurate prediction of citywide short-term metro passenger flow is essential to urban management and transport scheduling. Recently, an increasing number of researchers have applied deep learning models to passenger flow prediction. Nevertheless, the task is still challenging due to the complex spatial dependency on the metro network and the time-varying traffic patterns. Therefore, we propose a novel deep learning architecture combining graph attention networks (GAT) with long short-term memory (LSTM) networks, which is called the hybrid GLM (hybrid GAT and LSTM Model). The proposed model captures the spatial dependency via the graph attention layers and learns the temporal dependency via the LSTM layers. Moreover, some external factors are embedded. We tested the hybrid GLM by predicting the metro passenger flow in Shanghai, China. The results are compared with the forecasts from some typical data-driven models. The hybrid GLM gets the smallest root-mean-square error (RMSE) and mean absolute percentage error (MAPE) in different time intervals (TIs), which exhibits the superiority of the proposed model. In particular, in the TI 10 min, the hybrid GLM brings about 6–30% extra improvements in terms of RMSE. We additionally explore the sensitivity of the model to its parameters, which will aid the application of this model.

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Section: Machine Learning Methodsmentioning

confidence: 99%

A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow

Han

Peng

Wang³

et al. 2021

IJGI

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“…Bai et al. [12] considered the trend factor and time interval factor of passenger flow by using a kind of enhanced k‐nearest neighbours (KNN) based on the different attributes of each station on the Beijing subway line. Although the performance in [12] outperforms SARIMA and the original KNN model, the regression model, and KNN model require a large amount of computation, and the accuracy of the model is not high when dealing with big data problems.…”

Section: Related Workmentioning

confidence: 99%

Short‐term passenger flow forecasting using CEEMDAN meshed CNN‐LSTM‐attention model under wireless sensor network

2022

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For a long time, the accurate prediction of passenger flow can provide early warning information for various industries such as the public service industry, tourism industry, and industrial business, thus opportunely arranging passengers and providing homologous services to relieve the overloading of places and the accidents caused by overcrowding of people. In recent years, by using the wireless sensor network to sense the passenger data in advance, the technique of machine learning and neural networks has been utilized to assist the short‐term passenger flow prediction. In this study, building on convolutional neural network (CNN) and long short‐term memory network (LSTM), a complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and attention‐based CNN‐LSTM network to extract both temporal and spatial characteristics of passenger flow data, is proposed. Moreover, the problem of the inaccuracy of the noise part is properly solved by adding the CEEMDAN algorithm to the input layer. With the proposed network structure, the CNN‐LSTM network is replaced with the Conv‐LSTM network to reduce the information loss and get a further performance improvement. The result shows that 39% performance improvement can be achieved than the case with a single LSTM network, and 28% performance improvement can be achieved than the CNN‐LSTM network.

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“…Te k-nearest neighbor is a method that classifes every record in a dataset. Based on KNN, Bai et al take the trend factor and time interval factor of passenger fow into consideration, to reduce the risk that the original method has fewer evaluation criteria in the matching process [20]. Multilayer perceptron (MLP), a commonly used artifcial neural network with features such as adaptive and real-time learning, can be applied to trafc prediction [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)

et al. 2023

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Accurately predicting passenger flow at rail stations is an effective way to reduce operation and maintenance costs, improve the quality of passenger travel while meeting future passenger travel demand. The improvement of data acquisition capability allows fine-grained and large-scale built environment data to be extracted. Therefore, this paper focuses on investigating the relationship between the built environment around the station and the station passenger flow and discusses whether the built environment data can be applied to the station passenger flow prediction. Firstly, the evaluation system of station passenger flow influencing factors is built based on multisource data. The inner relationship between built environment factors and station passenger flow is investigated using the Pearson correlation analysis. Based on this, a multilayer perceptron (MLP)-based passenger flow prediction model was developed to predict the passenger flow at key stations. The study results show that the built environment factors impact station passenger flow, and the MLP prediction model has better prediction accuracy and applicability. The results of the study can be applied to predict the passenger flow scale of rail stations without historical passenger flow data and thus are also applicable to new rail stations.

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Short-Term Passenger Flow Forecast in Urban Rail Transit Based on Enhanced K-Nearest Neighbor Approach

Cited by 5 publications

References 2 publications

A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow

A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow

Short‐term passenger flow forecasting using CEEMDAN meshed CNN‐LSTM‐attention model under wireless sensor network

Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)

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