Subway Passenger Flow Forecasting With Multi-Station and External Factors

Yan, Dongpeng; Wang, Jing

doi:10.1109/access.2019.2914239

Cited by 14 publications

(6 citation statements)

References 25 publications

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“…The prediction of passenger flow is very similar to that of traffic flow [2][3][4][5]. Therefore, it is also possible to refer to the literature related to the prediction of traffic flow.…”

Section: Related Workmentioning

confidence: 75%

An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

Jiao

Huang

Song

et al. 2021

Sensors

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The COVID-19 pandemic is a significant public health problem globally, which causes difficulty and trouble for both people’s travel and public transport companies’ management. Improving the accuracy of bus passenger flow prediction during COVID-19 can help these companies make better decisions on operation scheduling and is of great significance to epidemic prevention and early warnings. This research proposes an improved STL-LSTM model (ISTL-LSTM), which combines seasonal-trend decomposition procedure based on locally weighted regression (STL), multiple features, and three long short-term memory (LSTM) neural networks. Specifically, the proposed ISTL-LSTM method consists of four procedures. Firstly, the original time series is decomposed into trend series, seasonality series, and residual series through implementing STL. Then, each sub-series is concatenated with new features. In addition, each fused sub-series is predicted by different LSTM models separately. Lastly, predicting values generated from LSTM models are combined in a final prediction value. In the case study, the prediction of daily bus passenger flow in Beijing during the pandemic is selected as the research object. The results show that the ISTL-LSTM model could perform well and predict at least 15% more accurately compared with single models and a hybrid model. This research fills the gap of bus passenger flow prediction under the influence of the COVID-19 pandemic and provides helpful references for studies on passenger flow prediction.

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“…The prediction of passenger flow is very similar to that of traffic flow [2][3][4][5]. Therefore, it is also possible to refer to the literature related to the prediction of traffic flow.…”

Section: Related Workmentioning

confidence: 75%

An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

Jiao

Huang

Song

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Reduction of train travel time had a significant effect on demand, but it was investigated that the decrease in average delay did not have a significant effect on demand. In addition, there are studies confirming that the correlation between stations is affected by the routes through the flow of passengers who want to transfer to other subway lines by focusing on the existence of complex correlations between stations in urban subway networks [10]. Xie et al [11] defined passenger flow as the amount, speed, and density of passenger flow and calculated a specific bottleneck of the railway system through simulation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparison into the Factors Affecting Urban Rail Systems: Local, Express, and High-Speed Rail in Tunnels at a Great Depth in a Metropolitan Area

et al. 2021

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In this study, the factors influencing the choice of the type of urban railroad transportation in the metropolitan areas of Korea were analyzed. As the populations of metropolitan areas are expanding, the importance of rail transportation, which has a high travel reliability in terms of travel time, has increased, and various types of railroad systems have emerged accordingly. This study was focused on the choice behavior of travelers on local and express trains that use the same track and differ only in the number of stations and operating times. To compare the choice behavior of travelers between local and express trains, factors such as the waiting time on the platform and the in-car travel time were considered. We also investigated the system choice behavior for an existing express subway and high-speed rail trains in tunnels at a great depth in terms of horizontal access time (walking), vertical access time, in-vehicle travel time, and travel fare. For a high-speed rail built underground at a great depth of 50 m, the stated preference survey was designed, and data were collected in consideration of the Great Train Express being promoted in the Seoul metropolitan area by the Korean government. The results of this study are expected to be considered important data for improving the rail system design from the user’s perspective to increase the demand for urban rail transportation in metropolitan areas.

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“…The passenger flow is defined as the number of boarding or alighting pedestrians at the target station during a constant interval in the prediction tasks [ 7 , 8 ]. In previous studies, the collection of passenger flows mainly includes two ways, as follows: Videos.…”

Section: Introductionmentioning

confidence: 99%

“…The task of passenger flow prediction is quite similar to traffic flow prediction [ 7 , 8 , 12 , 16 ], which is only different in the input data of the models. Therefore, many practical models of traffic flow prediction could be referred to as well.…”

Section: Introductionmentioning

confidence: 99%

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Zhou

Wang

Zhao

2020

Sensors

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The metro system plays an important role in urban public transit, and the passenger flow forecasting is fundamental to assisting operators establishing an intelligent transport system (ITS). The forecasting results can provide necessary information for travelling decision of travelers and metro operations of managers. In order to investigate the inner characteristics of passenger flow and make a more accurate prediction with less training time, a novel model (i.e., SSA-AWELM), a combination of singular spectrum analysis (SSA) and AdaBoost-weighted extreme learning machine (AWELM), is proposed in this paper. SSA is developed to decompose the original data into three components of trend, periodicity, and residue. AWELM is developed to forecast each component desperately. The three predicted results are summed as the final outcomes. In the experiments, the dataset is collected from the automatic fare collection (AFC) system of Hangzhou metro in China. We extracted three weeks of passenger flow to carry out multistep prediction tests and a comparison analysis. The results indicate that the proposed SSA-AWELM model can reduce both predicted errors and training time. In particular, compared with the prevalent deep-learning model long short-term memory (LSTM) neural network, SSA-AWELM has reduced the testing errors by 22% and saved time by 84%, on average. It demonstrates that SSA-AWELM is a promising approach for passenger flow forecasting.

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Subway Passenger Flow Forecasting With Multi-Station and External Factors

Cited by 14 publications

References 25 publications

An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

A Comparison into the Factors Affecting Urban Rail Systems: Local, Express, and High-Speed Rail in Tunnels at a Great Depth in a Metropolitan Area

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

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