Multi‐graph convolutional network for short‐term passenger flow forecasting in urban rail transit

Zhang, Jinlei; Chen, Feng; Guo, Yinan; Li, Xiaohong

doi:10.1049/iet-its.2019.0873

Cited by 103 publications

(47 citation statements)

References 38 publications

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“…For example, the long short-term memory (LSTM) network [11], which is based on RNN [12], was introduced to predict traffic speed at first, and some scholar has enhanced long-term features to improve prediction precision with LSTM [13]. RNN-based models cannot consider spatial correlations in a citywide network [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…e combination algorithm is derived, using the optimization algorithm to optimize the power value or threshold of the neural network to achieve a rapid convergence effect. At the same time, scholars hybrid some models to enhance the structural information, which will be lost during preprocessing, such as Res-LSTM [24], SVM-LSTM [25], Con-GCN [14], GA-LSTM [26], GATCN [27], PSO-LSTM [28], and TGC-LSTM [29].…”

Section: Literature Reviewmentioning

confidence: 99%

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Passenger Flow Prediction of Integrated Passenger Terminal Based on K-Means–GRNN

Tan

Liu

Yun

et al. 2021

Journal of Advanced Transportation

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As the passenger flow distribution center cooperating with various modes of transportation, the comprehensive passenger transport hub brings convenience to passengers. With the diversification of passenger travel modes, the passenger flow scale gradually increases, which brings significant challenges to the integrated passenger hub. Therefore, it is urgent to solve the problem of early warning and response to the future passenger flow to avoid congestion accidents. In this paper, the passenger flow GRNN prediction model is proposed, based on the K-means cluster algorithm, and an improved index named BWPs (Between-Within Proportion-Similarity) is proposed to improve the clustering effect of K-means so that the clustering effect of the new index is verified. In addition, the passenger flow data are studied and trained by combining with the GRNN neural network model based on parameter optimization (GA); the passenger flow prediction model is obtained. Finally, the passenger flow of Chengdu East Railway Station has been taken as an example, which is divided into 16 models, and each type of passenger flow is predicted, respectively. Compared with the traditional method, the results show that the model can predict the passenger flow with high accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Passenger Flow Prediction of Integrated Passenger Terminal Based on K-Means–GRNN

Tan

Liu

Yun

et al. 2021

Journal of Advanced Transportation

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show abstract

“…Chen et al applied the multitask learning technology based on the graph convolutional network to predict the taxi departure and arrival flows with real-world taxi trajectories from the city of Xi'an [29]. Zhang et al combined a graph convolutional network and a 3D CNN network to integrate the inflow and outflow information for the urban rail transit [34]. For more details, please refer to a comprehensive review by Wang et al [35].…”

Section: Introductionmentioning

confidence: 99%

Predicting Travel Demand of a Docked Bikesharing System Based on LSGC-LSTM Networks

et al. 2021

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The sustainable development of docked bikesharing systems has gained focus again owing to several problems in dockless bikesharing systems, including wanton destruction, theft, illegal parking, loss, and bankruptcy. The prediction of pickup/return demands is a critical issue for the sustainable operation of docked bikesharing systems. We propose a novel local spectral graph convolution (LSGC)-long short-term memory (LSTM) to predict pickup/return demands based on multi-source data. We apply LSGC to indicate the spatial dependency according to the geographic information system data that provide the location of stations, and we apply LSTM to demonstrate the temporal dependency based on the time-series data that represent pickup/return demands for public bikes. The LSGC-LSTM and six baseline models are trained with multi-source data of a month from a docked bikesharing system. The baseline models consist of a recurrent neural network, a LSTM, a gated recurrent unit, a graph attention LSTM network, an adaptive graph convolutional recurrent network, and a dynamic graph convolutional neural network. Results indicate that the LSGC-LSTM obtains a higher prediction accuracy and a higher efficiency than the baseline models.

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“…Principal Component Analysis (PCA) is another unsupervised method used by Luo, Cats, and van Lint (2017) to provide insight into the underlying structure of flow dynamics within a metro network. In the supervised framework, several researchers have investigated the development of models based on statistical and machine learning for the prediction of ridership in metro stations (Roos, Bonnevay, and Gavin 2016;Toque et al 2020) or in buses (Cui et al 2016;Zhang et al 2020).…”

Section: Introductionmentioning

confidence: 99%

What can we learn from 9 years of ticketing data at a major transport hub? A structural time series decomposition

Nailly

Côme

Samé

et al. 2021

Transportmetrica A: Transport Science

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Mobility demand analysis is increasingly based on smart card data, that are generally aggregated into time series describing the volume of riders along time. These series present patterns resulting from multiple external factors. This paper investigates the problem of decomposing daily ridership data collected at a multimodal transportation hub. The analysis is based on structural time series models that decompose the series into unobserved components. The aim of the decomposition is to highlight the impact of long-term factors, such as trend or seasonality, and exogenous factors such as maintenance work or unanticipated events such as strikes or the COVID-19 health crisis. We focus our analysis on incoming flows of passengers to two transport lines known to be complementary in the Parisian public transport network. The available ridership data allows analysis over both long-term and short-term time horizons including significant events that have impacted people's mobility in the Paris region.

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Multi‐graph convolutional network for short‐term passenger flow forecasting in urban rail transit

Cited by 103 publications

References 38 publications

Passenger Flow Prediction of Integrated Passenger Terminal Based on K-Means–GRNN

Passenger Flow Prediction of Integrated Passenger Terminal Based on K-Means–GRNN

Predicting Travel Demand of a Docked Bikesharing System Based on LSGC-LSTM Networks

What can we learn from 9 years of ticketing data at a major transport hub? A structural time series decomposition

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