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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]

In light of the problems of slow convergence speed, insufficient optimization accuracy and easy falling into local optima in the sparrow search algorithm, this paper proposes an adaptive sinusoidal-disturbance-strategy sparrow search algorithm (ASDSSA) and its mathematical equation. Firstly, the initial population quality of the algorithm is improved by fusing cubic chaos mapping and perturbation compensation factors; secondly, the sinusoidal-disturbance-strategy is introduced to update the mathematical equation of the discoverer's position to improve the information exchange ability of the population and the global search performance of the algorithm; finally, the adaptive Cauchy mutation strategy is used to improve the ability of the algorithm to jump out of the local optimal solutions. Through the optimization experiments on eight benchmark functions and CEC2017 test functions, as well as the Wilcoxon rank-sum test and time complexity analysis, the results show that the improved algorithm has better optimization performance and convergence efficiency. Further, the improved algorithm was applied to optimize the parameters of the long short term memory network (LSTM) model for passenger flow prediction on selected metro passenger flow datasets. The effectiveness and feasibility of the improved algorithm were verified by experiments. [ABSTRACT FROM AUTHOR]

The prediction and control of passenger flow in scenic spots is very important to the traffic management and safety of scenic spots. This study aims to predict the passenger flow of a scenic spot based on the passenger flow of the bus and subway stations around the scenic spots. We propose a passenger flow prediction model based on graph convolutional network–recurrent neural network (GCN–RNN). First, a "graph" is constructed according to the geographical relationship between the scenic spot and the surrounding bus and subway stations. Then, characteristics of surrounding areas of bus and subway stations are constructed based on the crowd behavior analysis, and these are then used as the node-information of the "graph". Last, the GCN–RNN model is used to extract the temporal and spatial characteristics of the passenger flow data of the scenic spot to realize the prediction. The experimental results show that the proposed model is effective in passenger flow prediction in scenic spots. [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]

Accurate subway passenger flow prediction is crucial to operation management and line scheduling. It can also promote the construction of intelligent transportation systems (ITS). Due to the complex spatial features and time-varying traffic patterns of subway networks, the prediction task is still challenging. Thus, a hybrid neural network model, GCTN (graph convolutional and comprehensive temporal neural network), is proposed. The model combines the Transformer network and long short-term memory (LSTM) network to capture the global and local temporal dependency. Besides, it uses a graph convolutional network (GCN) to capture the spatial features of the subway network. For the sake of the stability and accuracy for long-term passenger flow prediction, we enhance the influence of the station itself and the global station and combine the convolutional neural networks (CNN) and Transformer. The model is verified by the passenger flow data of the Shanghai Subway. Compared with some typical data-driven methods, the results show that the proposed model improves the prediction accuracy in different time intervals and exhibits superiority in prediction stability and robustness. Besides, the model has a better performance in the peak value and the period when passenger flow changes quickly. [ABSTRACT FROM AUTHOR]

In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space, an adaptive weighting factor balances the global and local search ability, and a variance operation combined with differential evolution is used to avoid local optimal traps. The experiment adopts variables such as network booking volume and search index as inputs for passenger flow prediction. The predator algorithm is trained by Extreme Learning Machine (ELM) to optimize the input weights and biases to build the FMMPAELM model. The results show that on the training samples, the FMMPA-ELM model predictions are highly consistent with the actual values, with a maximum prediction index of 200.On the test samples, although there are errors, the FMMPA-ELM model exhibits better prediction ability than the traditional ELM model. It is concluded that the FMMPAELM model can effectively improve the accuracy of passenger flow prediction and provide powerful decision support for the tourism industry.

This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning neural network named the graph convolutional memory network (GCMN) was constructed and trained for accurate real-time prediction of passenger traffic flow for the MRS. Data collected of the traffic flow in Delhi's metro rail network system in the period from October 2012 to May 2017 were utilized to demonstrate the effectiveness of the developed model. The results indicate that (1) the developed method provides accurate predictions of the traffic flow with an average coefficient of determination (R2) of 0.920, RMSE of 368.364, and MAE of 549.527, and (2) the GCMN model outperforms state-of-the-art methods, including LSTM and the light gradient boosting machine (LightGBM). This study contributes to the state of practice in proposing a novel framework that provides reliable estimations of passenger traffic flow. The developed model can also be used as a benchmark for planning and upgrading works of the MRS by metro owners and architects. [ABSTRACT FROM AUTHOR]

Predicting passenger flow is crucial for effective management and safety in railway stations. Accurate prediction of passenger flow facilitates effective allocation of staff tasks, enhances the efficient utilization of waiting areas, ensures passenger safety, and promotes a smooth travel experience for passengers. However, accurately establishing the spatial-temporal relationship of passenger flow within the station and predicting the passenger flow in each region and time period is challenging due to the varying waiting habits and train preferences of each individual passenger. In this paper, we propose a Residual-RNN-Channel Spatial-Temporal Graph Convolutional Network (RRC-STGCN), which utilizes the channel attention mechanism and residual structure. The model divides the data with a time dimension into multiple periods, capturing spatial-temporal correlations through the channel attention mechanism, and extracting spatial-temporal dependencies from the feature maps using the spatial-temporal convolution module. The model uses a residual structure to fuse features in order to enhance the accuracy of prediction results. In addition, we conduct a comprehensive experimental evaluation using a real dataset of railway station passenger, demonstrating that the RRC-STGCN model outperforms five well-known baselines. Moreover, we provide visualizations of the prediction results, effectively showcasing the dynamic changes in passenger flow in each waiting area.

• 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]

Featured Application: This study integrates diffusion convolution in a graph into a recurrent neural network to capture the spatiotemporal dependencies of different bus lines in a bus network for better passenger flow prediction. The proposed method is implemented in the bus network of Jiading, Shanghai, and achieves better modeling performance than that of the classic recurrent neural network models. The short-term bus passenger flow prediction of each bus line in a transit network is the basis of real-time cross-line bus dispatching, which ensures the efficient utilization of bus vehicle resources. As bus passengers transfer between different lines, to increase the accuracy of prediction, we integrate graph features into the recurrent neural network (RNN) to capture the spatiotemporal dependencies in the bus network. The diffusion convolution recurrent neural network (DCRNN) architecture is adopted to forecast the future number of passengers on each bus line. The demand evolution in the bus network of Jiading, Shanghai, is investigated to demonstrate the effectiveness of the DCRNN model. Compared with classic RNN models, our proposed method has an advantage of about 5% in mean average percentage error (MAPE). The incorporation of diffusion convolution shows that the travel demand in a bus line tends to be similar to that in the closely related lines. In addition, the improvement in MAPE shows that this model outputs more accurate prediction values for low-demand bus lines. It ensures that, for real-time cross-line bus dispatching with limited vehicle resources, the low-demand bus lines are less likely to be affected to maintain a decent level of service of the whole bus network. [ABSTRACT FROM AUTHOR]

With the refinement of the urban transportation network, more and more passengers choose the combined mode. To provide better inter-trip services, it is necessary to integrate and forecast the passenger flow of multi-level rail transit network to improve the connectivity of different transport modes. The difficulty of multi-level rail transit passenger flow prediction lies in the complexity of the spatiotemporal characteristics of the data, the different characteristics of passenger flow composition, and the difficulty of research. At present, most of the research focuses on one mode of transportation or the passenger flow within the city, while the comprehensive analysis of passenger flow under various modes of transportation is less. This study takes the key nodes of the multi-level rail transit railway hub as the research object, establishes a multi-task learning model, and forecasts the short-term passenger flow of rail transit by combining the trunk railway, intercity rail transit and subway. Different from the existing research, the model introduces convolution layer and multi-head attention mechanism to improve and optimize the Transformer multi-task learning framework, trains and processes the data of trunk railway, intercity railway, and subway as different tasks, and considers the correlation of passenger flow of trunk railway, intercity railway, and subway in the prediction. At the same time, a new residual network structure is introduced to solve the problems of over-fitting, gradient disappearance, and gradient explosion in the training process. Taking the large comprehensive transportation hub in Guangzhou metropolitan area as an example, the proposed multi-task learning model is evaluated. The improved Transformer has the highest prediction accuracy (Average prediction accuracy of passenger flow of three traffic modes) 88.569%, and others methods HA, FC-LSTM and STGCN are 81.579%, 82.230% and 81.761%, respectively. The results show that the proposed multi-task learning model has better prediction performance than the existing models. [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.

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.

Bus operation scheduling is closely related to passenger flow. Accurate bus passenger flow prediction can help improve urban bus planning and service quality and reduce the cost of bus operation. Using machine learning algorithms to find the rules of urban bus passenger flow has become one of the research hotspots in the field of public transportation, especially with the rise of big data technology. Bus IC card data are an important data resource and are more valuable to passenger flow prediction in comparison with manual survey data. Aiming at the balance between efficiency and accuracy of passenger flow prediction for multiple lines, we propose a novel passenger flow prediction model based on the point-of-interest (POI) data and extreme gradient boosting (XGBoost), called PFP-XPOI. Firstly, we collected POI data around bus stops based on the Amap Web service application interface. Secondly, three dimensions were considered for building the model. Finally, the XGBoost algorithm was chosen to train the model for each bus line. Results show that the model has higher prediction accuracy through comparison with other models, and thus this method can be used for short-term passenger flow forecasting using bus IC cards. It plays a very important role in providing decision basis for more refined bus operation management. [ABSTRACT FROM AUTHOR]

In light of the problems of slow convergence speed, insufficient optimization accuracy and easy falling into local optima in the sparrow search algorithm, this paper proposes an adaptive sinusoidal-disturbance-strategy sparrow search algorithm (ASDSSA) and its mathematical equation. Firstly, the initial population quality of the algorithm is improved by fusing cubic chaos mapping and perturbation compensation factors; secondly, the sinusoidal-disturbance-strategy is introduced to update the mathematical equation of the discoverer’s position to improve the information exchange ability of the population and the global search performance of the algorithm; finally, the adaptive Cauchy mutation strategy is used to improve the ability of the algorithm to jump out of the local optimal solutions. Through the optimization experiments on eight benchmark functions and CEC2017 test functions, as well as the Wilcoxon rank-sum test and time complexity analysis, the results show that the improved algorithm has better optimization performance and convergence efficiency. Further, the improved algorithm was applied to optimize the parameters of the long short term memory network (LSTM) model for passenger flow prediction on selected metro passenger flow datasets. The effectiveness and feasibility of the improved algorithm were verified by experiments.