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Accurate passenger flow forecasting in urban rail transit (URT) could provide a vital reference for operators' timely operation management. However, due to the enormous scale of the metro network, it is unwise to forecast passenger flows at the station scale individually. In this paper, a forecasting framework is proposed for network-scale forecasting tasks considering both accuracy and efficiency. There are mainly three stages in the forecasting framework. Firstly, three kinds of similarity measurements are presented regarding the adjacent similarity, geographic location similarity, and trend similarity. Secondly, three similarity graphs are formed by combining the three kinds of similarity measurements and flow time series. Thirdly, the multigraph network is applied to perform passenger flow forecasting. The experimental results indicated that the proposed method performs relatively accurately for the network-scale prediction with economic time costs. Specifically, the proposed model could acquire the feasible forecasts compared with the best disaggregate model using less than half of calculation costs, and above 10% reduction in root-mean squared error (RMSE) compared with the best aggregate model in the benchmark trials. Extensive contrast experiments were conducted to investigate the sensitivity and interpretability of the proposed model. [ABSTRACT FROM AUTHOR]

Passenger flow prediction is a critical approach to ensure the effective functioning of urban rail transit. However, there are few studies that combine multiple influencing factors for short-term passenger flow prediction. It is also a challenge to accurately predict passenger flow at all stations in the line at the same time. To overcome the above limitations, a deep learning-based method named ST-RANet is proposed, which consists of three spatio-temporal modules and one external module. The model is capable of predicting inbound and outbound passenger flow for all stations within the network simultaneously. We model the spatio-temporal data in terms of three temporal characteristics, including closeness, period, and trend. For each characteristic, we construct a spatio-temporal module that innovatively integrates the attention mechanisms into the middle of residual units and convolutional neural networks (CNNs) to extract and learn spatio-temporal features. Subsequently, the results of the three modules are integrated using a parameter matrix method, which allows for dynamic aggregation based on data. The integration results are further combined with external factors, such as holidays and meteorological information, to obtain passenger flow prediction values for each station. The proposed model is validated using real data from Beijing Subway, and optimized parameters are applied for 30-min granularity passenger flow predictions. Comparing the performance against 5 baseline models and verifying with data from multiple lines, the results indicate that the proposed ST-RANet model shows the best results. It is demonstrated that the method proposed in this paper has high prediction accuracy and good applicability. [ABSTRACT FROM AUTHOR]

The accurate prediction of passenger flow is crucial in improving the quality of the service of intercity high-speed railways. At present, there are a few studies on such predictions for railway origin–destination (O-D) pairs, and usually only a single factor is considered, yielding a low prediction accuracy. In this paper, we propose a neural network model based on multi-source data (NN-MSD) to predict the O-D passenger flow of intercity high-speed railways at different times in one day in the short term, considering the factors of time, space, and weather. Firstly, the factors that influence time-varying passenger flow are analyzed based on multi-source data. The cyclical characteristics, spatial and temporal fusion characteristics, and weather characteristics are extracted. Secondly, a neural network model including three modules is designed based on the characteristics. A fully connected network (FCN) model is used in the first module to process the classification data. A bi-directional Long Short-Term Memory (Bi-LSTM) model is used in the second module to process the time series data. The results of the first module and the second module are spliced and fused in the third module using an FCN model. Finally, an experimental analysis is performed for the Guangzhou–Zhuhai intercity high-speed railway in China, in which three groups of comparison experiments are designed. The results show that the proposed NN-MSD model can predict many O-D pairs with a high and stable accuracy, which outperforms the baseline models, and multi-source data are very helpful in improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurate short-time passenger volume prediction can guarantee the efficient scheduling command of urban rail transit. However, the short-time passenger flow of rail transit has the characteristics of nonlinearity and high randomness. In order to improve the prediction accuracy of short-time passenger volume, the Seasonal-Trend decomposition using Loess (STL) method and Human Evolutionary Optimization Algorithm (HEOA) is employed to optimize the bi-directional long short-term memory neural network (BiLSTM). Thus, a combined STL-HEOA-BiLSTM prediction model is proposed. Firstly, the inbound passenger volume along the urban rail transit is classified according to the Pearson correlation number. Secondly, the STL algorithm decomposes different types of short-time passenger flow data into Trend component (Tt), Seasonal component (St) and Residual component (Rt). Thirdly, the HEOA optimizes the various types of hyper-parameters of the BiLSTM model. Finally, the optimized BiLSTM model predicts Tt, St and Rt individually, and the final prediction value is obtained based on the combination of the three predictions. Three evaluation metrics are used to assess the results and quantify the effectiveness of the combined model. The example analysis shows that the prediction accuracy of the combined STL-HEOA-BiLSTM model surpasses that of the other six common and combined models in forecasting short-duration passenger flow. This experimental result shows the effectiveness, accuracy and applicability of the STL-HEOA-BiLSTM model proposed in this paper. It is demonstrated that the proposed model has a reference value for urban rail transit operators. [ABSTRACT FROM AUTHOR]

Station passenger float forecasting is a normal spatio-temporal statistics forecasting problem. Effectively capturing comprehensive spatio-temporal correlations in such data plays a key role in solving such problems. This paper proposes Spatio-Temporal Graph Convolutional Neural Network Based on Periodic Component (Periodic ST-GCN) to predict the passenger glide at public transportation stations. The model now not solely captures the spatio-temporal traits of visitors' facts through the spatio-temporal convolution block with a sandwich shape composed of one spatial-dimensional convolution and two temporal dimensional convolutions. Also, it effectively considers the periodicity of passenger flow at public transportation stations through the recent, daily and weekly periodic components and, because the graph convolution in the spatial dimension uses pure convolution operations, it reduces the model' training parameters and converges faster. Through the experiment of predicting the Origin–Destination (OD) of passenger flow at public transportation stations in Chongqing, it is found that Periodic ST-GCN achieves better results in two evaluation indicators, mean absolute error (MAE) and root mean square deviation (RMSE). [ABSTRACT FROM AUTHOR]

Effective management of urban rail transit systems increasingly centers on short-term passenger flow prediction which is a key factor in operational scheduling decisions. The critical need to use existing infrastructure efficiently and avoid potential emergencies due to large crowd gatherings. The advancements in short-term passenger flow forecasting have become essential within the intelligent transportation system domain. Despite its importance, there is a prominent gap in research specifically addressing the prediction of various types of passenger flows in subway systems. To bridge the gap, the paper proposed an innovative integrated method combining CNN-LSTM and SVM with Wavelet Transform harnessing their collective strengths. The approach involves a multi-stage process. Initially, passenger flow data is dissected into high and low-frequency series using wavelet transform. In the prediction phase, CNN-LSTM and SVM techniques are employed to, respectively learn and forecast these high and low-frequency sequence. The final phase involves the reintegration of these various predicted sequences through wavelet transform. The integrated approach aims to provide a more accurate prediction of short-term passenger flows in urban rail transit catering to the specific demands of the complex and dynamic field. The analysis shows that the SVM based Wavelet Transform method outperforms the CNN-LSTM based Wavelet Transform method in terms of efficiency and accuracy. Both approaches achieve better results than classical prediction techniques for passenger flow prediction on evaluation metrics, i.e., MAPE (9.80% and 7.28%), VAPE (0.78% and 3.85%), RMSE (2.48% and 0.76%), MSE (14.86% and 4.65%), R2 (1.57% and 12.57%) and accuracy (85.00% and 90.00%) respectively. [ABSTRACT FROM AUTHOR]

With the development of modern cities, urban rail transit has become an indispensable part of residents' travelling mode, and accurate prediction of urban rail transit passenger flow is particularly important. However, due to the non-linearity and non-stability of passenger flow, the low quality of big data and the lack of data make it more and more difficult to predict the passenger flow of urban rail transit. In this paper, we propose a unique model structure that ingeniously integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN) with fused residual network connections, and a self-attention mechanism. The design of this model is primarily to effectively handle complex and low-quality data, which is often prevalent in passenger flow prediction scenarios. To validate the effectiveness of the proposed model, we used data from the Beijing urban rail transit in 2016 for prediction. In the experiments, we performed comparison study and ablation study. The experimental results show that the model proposed in this paper has significantly improved prediction accuracy in both horizontal and vertical comparisons. This outcome substantiates that the proposed model can not only effectively handle complex and low-quality data but also extract short-term features and long-term dependency features of passenger flow well, thereby achieving more accurate predictions. • Integration of GRU, CNN with fused residual connections, and self-attention mechanism constitutes a novel model. • The model is capable of handling complex and lower-quality data, significantly improving the accuracy of predictions. • The model can simultaneously extract short-term features and long-term dependency features from time series. [ABSTRACT FROM AUTHOR]

Predicting the passenger flow of public transport in a newly developed area of a city is very urgent for designing a precise and efficient public transport network. This paper proposes a new prediction model by exploring the relationship between the passenger flow of a station and its surrounding area's factors. First, in order to obtain more accurate factors affecting the passenger flow, the city is divided into multiple regions with similar internal traffic properties and moderate spatial size using the data of urban road network and buildings. Second, to effectively solve the problem of fuzziness of the station's attraction scope, the concept of the membership degree and fuzzy processing method is proposed. Finally, the station's passenger flow prediction model is launched based on Xgboost. The experimental results on three districts in Beijing show that our method outperforms all baselines significantly, which improves the accuracy by more than 20%. [ABSTRACT FROM AUTHOR]

It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and the long short-term memory network (LSTM) based on machine learning is proposed. The model couples the external factors such as date attributes, weather conditions, and air quality, to improve the overall prediction performance and solve the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. Using the swiping data and related weather information of some stations of Chongqing Rail Transit Line 3, the TCN-LSTM model is verified by an example, and the prediction results of the single LSTM model are given for comparison. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times. Compared with the single LSTM model, the TCN-LSTM model has better prediction accuracy and data generalization ability. [ABSTRACT FROM AUTHOR]

• This paper models the metro system as knowledge graph for passenger flow prediction. • It combines traffic patterns and land-use features for knowledge graph construction. • It proposes a SARGCN model for spatiotemporal prediction on metro knowledge graphs. • It uses an attention mechanism to learn the correlation between inflow and outflow. • Validated on two metro datasets, it outperforms numerous advanced baselines. With the rapid development of intelligent operation and management in metro systems, accurate network-scale passenger flow prediction has become an essential component in real-time metro management. Although numerous novel methods have been applied in this field, critical barriers still exist in integrating travel behaviors and comprehensive spatiotemporal dependencies into prediction. This study constructs the metro system as a knowledge graph and proposes a split-attention relational graph convolutional network (SARGCN) to address these challenges. Breaking the limitations of physical metro networks, we develop a metro topological graph construction method based on the historical origin–destination (OD) matrix to involve travel behaviors. Then, we design a metro knowledge graph construction method to incorporate land-use features. To adapt prior knowledge of metro systems, we subsequently propose the SARGCN model for network-scale metro passenger flow prediction. This model integrates the relational graph convolutional network (R-GCN), split-attention mechanism, and long short-term memory (LSTM) to explore the spatiotemporal correlations and dependence between passenger inflow and outflow. According to the model validation conducted on the metro systems in Shenzhen and Hangzhou, China, the SARGCN model outperforms the advanced baselines. Furthermore, quantitative experiments also reveal the effectiveness of its component and the constructed metro knowledge graph. [ABSTRACT FROM AUTHOR]

Metro system has been increasingly recognized as a backbone of urban transportation system in many cities around the world. To improve the demand management and operation efficiency, it is crucial to have accurate prediction of real-time metro passenger flow. However, the forecast performance is often subject to the complex spatial and temporal distributions of the metro passenger flow data. To this end, we developed a novel dual attentive graph neural network that can effectively predict the distribution of metro traffic flow considering the spatial and temporal influences. Specifically, two directed complete metro graphs (i.e., inbound and outbound graphs) and the weighted matrix of them are proposed to characterize the inbound (entering the system) and outbound (leaving the system) passenger flow, respectively. The weighted matrix of inbound graph is estimated based on the historical origin-destination demand and that of the outbound graph is estimated based on the similarity metrics between every two stations. Moreover, to capture the dependencies between inbound and outbound flows, multi-layer graph spatial attention networks that incorporate the spatial context are applied to exploit the dynamic inter-station correlations. Then, the acquired dependency features integrated with external factors, such as weather conditions, are filtered by temporal attention and fed into a sequence decoder to produce short-term and long-term passenger flow predictions. Finally, a series experiments are conducted based on a comprehensive empirical dataset. Findings indicated that the proposed model does not only well predict the metro passenger flow, but also effectively detect the emergencies and incidents of metro system. [ABSTRACT FROM AUTHOR]