Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model

Li, Fulin; Wang, Yong Gang; Zhong, Gang; Zhang, Jian; Ran, Bin

doi:10.1007/s12205-017-1016-9

Cited by 51 publications

(30 citation statements)

References 21 publications

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“…an outburst of passengers. Passenger flow forecasting is often largely inaccurate due to various uncertainties (Li, L. et al, 2018;Zhao et al, 2011) that merit special consideration in ATC application. In this paper, we simultaneously propose a passenger flow forecast method comprised of a novel Markov-Grey model which includes modification factors δ 1 and δ 2 , as determined by two minimum mean square error principles, to improve the forecast precision; the Markov approach is utilized to further process the residual error series.…”

Section: Resultsmentioning

confidence: 99%

“…As discussed in the Introduction, passenger flow volume forecasting has long been regarded as a critical requirement for mass rail transit scheduling and operations management (Leng et al, 2013;Li, L. et al, 2018). We selected Xiaozhai station, a major transfer station of Metro Line 2, as a case study and gathered its daily passenger flow data for September, 2015.…”

Section: Illustrative Example and Analysismentioning

confidence: 99%

“…Previous researchers have established several approaches to passenger flow forecasting, but from a more general perspective (Zhao et al, 2011). For example, data sequences are generally used and mathematical modeling is generally the focus in developing these techniques (Leng et al, 2013;Li, L. et al, 2018), but they are lacking in accuracy overall. Grey system theory is a multidisciplinary theory that was firstly proposed in early 1980s by Deng to describe systems such as passenger flow (Deng, 1982); the theory has indeed proven superior to conventional statistical models in dealing with systems that have both partially known and partially unknown parameters (Liu et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metro Passenger Flow Forecast with a Novel Markov-Grey Model

Wang

Zhang

2019

Period. Polytech. Transp. Eng.

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Accurate forecasts of passenger flow entering and leaving metro stations is an important work for Metro operation management, such as for the automatic adjustment of train operation diagrams or station passenger crowd regulation planning measures. In this study, Grey theory is introduced to develop a time series GM (1, 1) model for total passenger forecasting. Two modification factors determined by two minimum mean square error principles are proposed to decrease the discreteness of input data and thus improve the forecast accuracy. Moreover, the Markov chain approach is further used to optimize the residual error series. Passenger flow data entering and leaving the Xiaozhai station of Xi'an Metro Line 2 from September 1-30, 2015, were utilized to verify the effectiveness of the proposed method; the forecast results show that this novel Markov-Grey model performs well in terms of forecast accuracy with smaller SMSE and MAPE values. To this effect, the proposed method is especially well-suited to smooth passenger flow forecasting compared to other forecast techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Illustrative Example and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Metro Passenger Flow Forecast with a Novel Markov-Grey Model

Wang

Zhang

2019

Period. Polytech. Transp. Eng.

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“…The performance of the hybrid model was compared with ARIMA model and BP neural network using the actual data of Xi'an metro line 1. The results show that the hybrid model is superior to the other two models [3].…”

Section: Introductionmentioning

confidence: 92%

Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

2019

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The rapid development of cities has brought new challenges and opportunities to traditional traffic management. The usage of smart cards promotes the upgrading of intelligent transportation systems, and also produces considerable big data. As an important part of the urban comprehensive transportation system, Nanjing metro has more than 1 million inbound and outbound records of traffic smart cards used by residents every day. How to process these traffic data and present them visually is an urgent problem in modern traffic management. In this study, five working days with normal weather conditions in Nanjing were selected, and the swiping records of the smart cards were extracted, and the space–time characteristics were analyzed. In terms of time analysis, this research analyzed the 24-h fluctuation of daily average passenger flow, peak hour coefficient of passenger flow, 24-h fluctuation of passenger flow on different metro lines, passenger flow intensity on different metro lines and passenger flow comparison at different stations. In spatial analysis, this study uses thermodynamic charts to represent the inflow and outflow of passengers at different stations during early and evening peak periods. The analysis results and visualized images directly reflect the area where Nanjing metro congestion is located, and also shows the commuting characteristics of residents. It can solve the problem of urban congestion, carry out the rational layout of urban functional areas, and promote the sustainable development of people and cities.

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“…Also, a study proposed that space-time autoregressive integrated moving average methodology can be applied on pattern prediction [10]. In addition, many studies proposed hybrid models combining ARIMA with other models, which have better performance than single models [11], [12].…”

Section: B Related Researchmentioning

confidence: 99%

RNN-Based Subway Passenger Flow Rolling Prediction

Sha

Zhang

et al. 2020

IEEE Access

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The subway station passenger flow prediction model can forecast passenger volume in the future. This model helps to carry out safety warnings and evacuation of passenger flow in advance. Based on the data of the Shanghai traffic card, the passenger volume in all the time intervals is clustered into three different models for prediction. Taking the Nanjing East Road Station in Shanghai as an example, the time series of passenger volumes was combined with weather data to create several supervised sequences and was converted to supervised sequences according to different values of timestep. To accelerate convergence, two artificial features were added as input. The gated recurrent unit (GRU) network model achieves accurate rolling prediction from 15 minutes to 6 hours. Finally, comparing it with the long short-term memory (LSTM) network and the back-forward propagation network (BPN), it was confirmed that the GRU network with a timestep of 1.5 hours is the best model for the long-term (more than 3 hours) traffic flow rolling prediction, while GRU with a timestep of 45 minutes has the best result for short-term rolling prediction. INDEX TERMS Data analysis, time series analysis, predictive models, neural networks, LSTM, GRU.

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Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model

Cited by 51 publications

References 21 publications

Metro Passenger Flow Forecast with a Novel Markov-Grey Model

Metro Passenger Flow Forecast with a Novel Markov-Grey Model

Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

RNN-Based Subway Passenger Flow Rolling Prediction

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