Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system

Hao, Siyu; Lee, Der-Horng; Zhao, De

doi:10.1016/j.trc.2019.08.005

Cited by 196 publications

(78 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several recent works [28][29][30][31][32][33][34][35] specifically paid attention to the metro scenario. [28] addressed the crowd flow distribution prediction problem across the entire train network.…”

Section: Related Workmentioning

confidence: 99%

“…[33] gave an empirical study to predict MSP flows, but the proposed model can hardly handle the nonlinearity and complexity of metro traffic data. [34] employed a sequence learning model to predict the passenger flows but neglected the long-term periodicity and spatial correlation of MSP flows. Furthermore, [35] modeled the MSP flows as the weakly-dependent tensor data and designed a tensor completion algorithm to address the prediction problem.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

Sun

Zhu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

Recent years have witnessed a drastic increase in the number of urban metro passengers, which inevitably causes the overcrowdedness in the metro systems of many cities. Clearly, an accurate prediction of passenger flows at metro stations is critical for a variety of metro system management operations, such as line scheduling and staff preallocation, that help alleviate such overcrowdedness. Thus, in this paper, we aim to address the problem of accurately predicting metro station passenger (MSP) flows. Similar to other traffic data, such as road traffic volume and highway speed, MSP flows are also spatial-temporal in nature. However, existing methods for other traffic prediction tasks are usually suboptimal to predict MSP flows due to MSP flows' unique spatial-temporal characteristics. As a result, we propose a novel deep learning framework STP-TrellisNets, which for the first time augments the newly-emerged temporal convolutional framework TrellisNet for spatial-temporal prediction. The temporal module of STP-TrellisNets (named CP-TrellisNets) employs two TrellisNets in serial to jointly capture the short-and long-term temporal correlation of MSP flows. In parallel to CP-TrellisNets, its spatial module (named GC-TrellisNet) adopts a novel transfer flow-based metric to characterize the spatial correlation among MSP flows, and implements multiple diffusion graph convolutional networks (DGCNs) in time-series order with their outputs connected to a TrellisNet to capture the dynamics of such spatial correlation. Clearly, GC-TrellisNet essentially integrates TrellisNet with graph convolution, and empowers TrellisNet with the ability to capture dynamic graph-structured correlation. We conduct extensive experiments with two large-scale real-world automated fare collection datasets, which contain respectively about 1.5 billion records in Shenzhen, China and 70 million records in Hangzhou, China. The experimental results demonstrate that STP-TrellisNets outperforms the state-of-the-art baselines. CCS CONCEPTS • Information systems → Spatial-temporal systems; Data mining; • Computing methodologies → Neural networks.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

Sun

Zhu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

show abstract

“…The combined forecasting model combines several algorithms that perform well to effectively overcome the limitations of a single algorithm in forecasting and improve forecast accuracy. Hao et al [21] analyzed the short-term passenger flow changes in railway station, and analyzed the short-term historical passenger flow changes in detail. Guo et al [22] studied the short-term prediction method of interval passenger flow, and selected the BP neural network based on the characteristics of interval passenger flow itself; the interval passenger flow forecasting was realized with Matlab, and a comparative analysis was performed, empirical research shows that the characteristics of interval passenger flow data and neural network have good prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Neural Network-Based Prediction Model for Passenger Flow in a Large Passenger Station: An Exploratory Study

Jing

Yin

2020

IEEE Access

View full text Add to dashboard Cite

As the hub and carrier to transfer the passengers, the railway station is an important factor that affects the rail passenger transportation because the normal operation of the station without load redundancy is determined by the moderate passenger flow. It means reasonable and accurate prediction of passengers entering and leaving the station can provide the basis and guarantee for the station security, the resources allocation and the personnel deployment. Since the neural network model is good at processing the common regular data changes through training the network and adjusting the weight value based on a large number of training samples, the neural network model is used in processing the short-term irregular data to predict the passenger flow at the railway station which is susceptible to the constantly changing external factors. In this paper, the neural network is used to predict the passenger flow. First, the key factors affecting the change of the passenger flow are selected and analyzed as the input of the neural network. Second, the learning and the rate updating of variable step size are adopted to estimate the number people entering the station during a certain time interval, which is then weighted with the historical data to derive the prediction of the passenger flow during the next time interval. The simulation results show that the experiment results show that the method proposed in this paper can better track and predict the sudden changes in the passenger flow caused by emergencies. Meanwhile, it can be found that in the process of forecasting abnormal passenger flow, the most critical link is to summarize and summarize the characteristics of railway station passenger flow, clarify the type and time distribution of passenger flow at each station, and analyze the factors that cause abnormal changes in passenger flow.

show abstract

“…Deep learning models have gained increasing interest for its extraordinary mapping ability for big data. For example, Hao et al [22] constructed a sequence to sequence model embedded with the attention mechanism to predict alighting passengers in a largescale metro system; Polson et al [23] developed a deep neural network (DNN) to predict traffic flow under abnormal conditions; Tsai et al [24] combined simulated annealing (SA) algorithm and DNN to predict bus passenger demand; Zhang et al [25] utilized a spatial-temporal graph inception residual network to predict the network-based traffic flow. Deep belief network (DBN) [26], LSTM NN [27], radial basis function networks (RBFNN) [28] were also reported in literature.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Passenger Flow Prediction With Decomposition in Urban Railway Systems

Zhao

Yang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Accurate prediction of short-term passenger flow is vital for real-time operations control and management. Identifying passenger demand patterns and selecting appropriate methods are promising to improve prediction accuracy. This paper proposes a hybrid prediction model with time series decomposition and explores its performance for different types of passenger flows with varied characteristics in urban railway systems. The Seasonal and Trend decomposition using Loess (STL) is used to decompose the passenger flow into the seasonal, trend, and residual time series, representing the constant, long-term fluctuant, and stochastic passenger demand patterns. The approximate entropy (ApEn) is utilized to quantify the predictability of each component. After that, the Holt-Winters (HW) method is developed to predict the seasonal and trend components that both with high predictability. The long short-term memory networks (LSTM NN) are proposed to predict the residual with low predictability. The outputs from these models are combined to predict the short-term passenger flow. We assess the model performance with entry, exit, and transfer passenger flows at the target stations, which are selected via the agglomerative hierarchical clustering (AHC) algorithm in the Shanghai Metro. Compared with three representative models, the results show that 1) the decomposition-based hybrid model performs well for both one-step and multi-step predictions in terms of accuracy and robustness; 2) ApEn is useful for choosing appropriate prediction models. INDEX TERMS Short-term metro passenger flow prediction, Seasonal and Trend decomposition using Loess (STL)-based model, approximate entropy (ApEn), agglomerative hierarchical clustering (AHC) algorithm I. Yangyang Zhao received the B.E. degree and the M.E. degree in transportation engineering from Chang'an University, Xi'an, Shaanxi, China, in 2014 and 2017. He is currently pursuing the Ph.D. degree in transportation planning and management with the school of transportation and logistics,

show abstract

Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system

Cited by 196 publications

References 37 publications

STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

Neural Network-Based Prediction Model for Passenger Flow in a Large Passenger Station: An Exploratory Study

Short-Term Passenger Flow Prediction With Decomposition in Urban Railway Systems

Contact Info

Product

Resources

About