A method for short-term passenger flow prediction in urban rail transit based on deep learning

Dong, Ningning; Li, Tiezhu; Liu, Tianhao; Tu, Ran; Lin, Fang; Liu, Hui; Bo, Yiyong

doi:10.1007/s11042-023-14388-z

Cited by 10 publications

(6 citation statements)

References 33 publications

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“…So, some studies focus on multi-station passenger flow prediction, which individually predicts passenger flow for each cluster comprising multiple stations. For instance, Dong et al [23] employed K-Means to classify all stations into different categories and created a prediction model for the same category in the network. Liu et al [24] proposed a novel two-step model that predicted the passenger flow for each type of classification result.…”

Section: Reference Descriptionmentioning

confidence: 99%

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Li,

Liu,

2023

Applied Sciences

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Accurate prediction of metro passenger flow helps operating departments optimize scheduling plans, alleviate passenger flow pressure, and improve service quality. However, existing passenger flow prediction models tend to only consider the historical passenger flow of a single station while ignoring the spatial relationships between different stations and correlations between passenger flows, resulting in low prediction accuracy. Therefore, a multi-scale residual depthwise separable convolution network (MRDSCNN) is proposed for metro passenger flow prediction, which consists of three pivotal components, including residual depthwise separable convolution (RDSC), multi-scale depthwise separable convolution (MDSC), and attention bidirectional gated recurrent unit (AttBiGRU). The RDSC module is designed to capture local spatial and temporal correlations leveraging the diverse temporal patterns of passenger flows, and then the MDSC module is specialized in obtaining the inter-station correlations between the target station and other heterogeneous stations throughout the metro network. Subsequently, these correlations are fed into AttBiGRU to extract global interaction features and obtain passenger flow prediction results. Finally, the Hangzhou metro passenger inflow and outflow data are employed to assess the model performance, and the results show that the proposed model outperforms other models.

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Section: Reference Descriptionmentioning

confidence: 99%

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Li,

Liu,

2023

Applied Sciences

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“…More and more scholars have proposed methods for urban rail transit passenger flow prediction, such as Funchan et al used a variety of passenger flow prediction algorithm models to analyze and compare the results of passenger flow forecasting [1] ; Roos et al propose a Bayesian network model [2] ; Wei Lingxiang et al established a hybrid model based on temporal patterns attention mechanism and long and short-term memory networks to improve the utility and accuracy of the model [3] ; Xin Wang et al proposed a prediction architecture incorporating multiple passenger flow features based on deep learning and integrated learning [4] ; Dong, Ningning et al proposed a spatio-temporal network long and short-term memory model (TNS-LSTM) model to solve the prediction gap of underground entrance and exit passenger flow [5] ; Lv et al fused point-of-interest (poi) data to build an extreme gradient boosting (XGBoost) passenger flow prediction model, PFP-XPOI, which can be used for more refined operation management [6] . Therefore, based on the Hangzhou Metro swipe card data, we can analyse the trend and characteristics of passenger flow changes, construct various algorithmic models for short-term passenger flow prediction, evaluate the prediction results and verify the accuracy of the model.…”

Section: Introductionmentioning

confidence: 99%

Research on passenger flow forecasting model for urban rail transport

Cui

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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The trend and periodicity in metro passenger flow series data can provide some predictive power. Using AFC data, correlation analysis is used to verify the degree of correlation between current and historical passenger flow data. Through analysis of the original sample data and extraction of passenger flow features, Random Forest (RF), Long Short-Term Memory Networks (LSTM) and extreme gradient boosting (XGBoost) can be constructed respectively. The prediction model was used to forecast the future day rail passenger flow, and the accuracy of the model was evaluated by the mean absolute error, root mean square error and coefficient of determination. The model prediction and evaluation results show that the XGBoost model performs better, and the errors of the evaluation indicators are smaller than those of other models, and the algorithm is more accurate.

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“…The suggested method employs fusion deep learning techniques to improve the precision and resilience of closed-loop detection for robot navigation. Dong et al, [10] shaped the temporal-spatial network long short-term memory model to anticipate metro inbound/outbound passenger movement. The suggested model is compared to four prediction models, including baseline, ARIMA, SVR, and LSTM.…”

Section: Introductionmentioning

confidence: 99%

Passenger Flow Prediction Method based on Hybrid Algorithm: Intelligent Transportation System

Raza,

Liu,

Adeke

et al. 2024

ejaset

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Forecasting passenger flow at metro transit stations is a useful method for optimizing the organization of passenger transportation and enhancing operational safety and transportation efficiency. Aiming at the problem that the traditional ARIMA model has poor performance in predicting passenger flow, a hybrid prediction method based on ARIMA-Kalman filtering is proposed. In this regard, ARIMA model training experimental samples are integrated with Kalman filter to create a prediction recursion equation, which is then utilized to estimate passenger flow. The simulation experiment results based on the inbound passenger flow data of Nanjing metro station show that compared with the single ARIMA model, the root mean square error of the prediction results of the proposed ARIMA-Kalman filter hybrid algorithm is reduced by 257.106, and the mean absolute error decreased by 145. 675, the mean absolute percentage error dropped by 5. 655%, proving that the proposed hybrid algorithm has higher prediction accuracy. The experiment results based on the passenger flow data of Nanjing metro station show that compared to a single ARIMA model, the proposed ARIMA Kalman filtering hybrid algorithm reduces the root mean square error of the prediction results by 257.106, the average absolute error by 145.675, and the average absolute percentage error by 5.655%. It has been proven that the proposed hybrid algorithm has higher prediction accuracy.

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A method for short-term passenger flow prediction in urban rail transit based on deep learning

Cited by 10 publications

References 33 publications

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Research on passenger flow forecasting model for urban rail transport

Passenger Flow Prediction Method based on Hybrid Algorithm: Intelligent Transportation System

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