Short-term Passenger Flow Prediction Research Articles

Research on short-term passenger flow prediction of urban rail transit based on ARIMA algorithm

Research on short-term passenger flow prediction of urban rail transit based on ARIMA algorithm

Graph transformer embedded deep learning for short-term passenger flow prediction in urban rail transit systems: A multi-gate mixture-of-experts model

Urban rail transit (URT) plays a crucial role in mitigating urban traffic congestion by offering faster and higher-quality travel services. Short-term passenger flow predictions have practical significance for metro management and operation. However, the complex spatiotemporal characteristics and the relationship between entry and exit passenger flows make it challenging to detect the dynamic evolution patterns. This study proposes a Spatio-Temporal Graph Transformer (STGT) under the multi-task learning framework, utilizing Graph Transformer network and gated residual units to select and aggregate features. To account for the correlation between entry and exit passenger flow prediction tasks, the STGT model integrates a Multi-gate Mixture-of-Experts (MMoE) approach, which combines different expert networks for diverse input and explicitly learns to model passenger flow relationships in various scenarios. Metro-related characteristics such as weather conditions, train operation characteristics, and accessibility of nearby bus stops are incorporated to enhance prediction accuracy. Experimental evaluations are conducted using real-world historical passenger travel records from the Beijing subway. The results demonstrate the superior robustness and advantages of the STGT-MMoE model over basic and advanced benchmarks for passenger flow prediction tasks. These findings provide compelling evidence to address the challenges of short-term inflow and outflow prediction in urban rail transit systems.

COV-STFormer for Short-Term Passenger Flow Prediction During COVID-19 in Urban Rail Transit Systems

COV-STFormer for Short-Term Passenger Flow Prediction During COVID-19 in Urban Rail Transit Systems

Improved Long-Term Forecasting of Passenger Flow at Rail Transit Stations Based on an Artificial Neural Network

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations.

Learning spatial–temporal pairwise and high-order relationships for short-term passenger flow prediction in urban rail transit

Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize urban rail transit (URT) system resource allocation. Although graph-based models have been widely used in STPFP because of their ability to model complex spatial–temporal dependencies, accurate STPFP still faces challenges. The graph-based methods exploit pairwise stations to model spatial dependencies without considering the non-pairwise high-order relationships between multiple stations. Besides, the event’s occurrence will make the passenger flow at a particular moment have a more profound impact on the passenger flow at a future moment. To solve these issues, a spatial–temporal hypergraph attention recurrent network (STHGARN) method is proposed for STPFP. Specifically, STHGARN first models passenger flow data’s recent, daily, and weekly characteristics through three parallel components. Then, each parallel component uses the hypergraph attention recurrent network (HGARN) cell to jointly learn the pairwise and high-order relationships of multiple stations. Finally, a temporal Hawkes attention mechanism is constructed and concatenated after each parallel HGARN cell to simulate the influence of events and capture the historical key moments that affect the future passenger flow. Numerous experiments on three metro ridership datasets show that STHGARN is superior to the most advanced methods, and good prediction results can lay the foundation for the operation and management of URT.

Short-Term Passenger Flow Prediction in Urban Rail Transit Based on Points of Interest

Short-Term Passenger Flow Prediction in Urban Rail Transit Based on Points of Interest

基于GCN-CNN模型的轨道交通短时客流预测

基于GCN-CNN模型的轨道交通短时客流预测

Time Granularity Setting Principle for Short-Term Passenger Flow Prediction in Urban Rail Transit

Time Granularity Setting Principle for Short-Term Passenger Flow Prediction in Urban Rail Transit

Short-term Passenger Flow Prediction for Hangzhou Metro based on Machine Learning Algorithms

Subways have now reached an irreplaceable position in urban rail transportation. The aggregation of people choosing subway travel at the same time can easily lead to passenger congestion, so predicting passenger flow in advance is crucial. This study used LSTM (Long Short-Term Memory) as the data prediction model to forecast the card-swiping data of the Hangzhou subway for 10 consecutive days. The historical data was divided into intervals of 3 minutes, and the random forest was used to compare the correlation among features. The final prediction results showed that both the inbound and outbound flows exhibited a bimodal pattern on weekdays, while on holidays, the prediction results showed a bimodal pattern. Furthermore, by adjusting the parameters appropriately, the simulated trends closely matched the actual values, indicating the applicability of LSTM in simulating short-term passenger flow. Accurate prediction of passenger flow can enable more comprehensive management of the subway system and provide passengers with a higher-quality service experience and travel options.

Short-term Passenger Flow Prediction of Urban Rail Transit based on ARIMA Model

Urban rail transit is very important in modern urban transportation, which meets the needs of People's Daily travel. This paper aims to introduce the ARIMA model into the short-term passenger flow forecast of urban rail transit, analyze its principle and application method, and build ARIMA model combined with AFC data to forecast short-term passenger flow of urban rail transit. Through the research and improvement of ARIMA model, it is anticipated that it would give urban rail transport operators more precise and trustworthy short-term passenger flow forecasting capabilities, so as to realize more intelligent and efficient operation of urban rail transit system. In this study, the ARIMA model was established, and rigorous scientific methods such as residual test, autocorrelation function (ACF) and partial autocorrelation function (PACF) test, and comparison of predicted value and real value were adopted to conduct tests step by step according to the experimental process. Finally, it was confirmed that the excellent prediction accuracy of the ARIMA prediction model used in this paper, can provide accurate prediction results, and can be used in practical scenarios.

A Dual-View Approach for Multistation Short-Term Passenger Flow Prediction in Bus Transit Systems

Timely and accurate prediction of bus passenger flow plays a crucial role in uncovering real-time traffic demand, presenting an essential and formidable challenge in the realm of bus scheduling and management. The extensive application of deep learning methods in transit passenger flow prediction can be attributed to their exceptional ability to effectively capture spatiotemporal features, resulting in superior performance. However, prevailing deep learning models in transit passenger flow prediction tend to ignore the data enhancement. Additionally, the predominant focus on a single station in the prediction task presents challenges in effectively capturing the spatiotemporal features of the entire network. A model named TSD-ST is proposed to better accomplish the task of predicting short-term transit passenger flow at multistation. The TSD-ST model leverages time series decomposition for data enhancement. Simultaneously, in addition to considering the adjacency graph, the similarity of all the stations of the entire transit network is also considered and uses multigraph convolution and graph fusion modules. This approach enables the TSD-ST model to effectively capture spatiotemporal dependencies. Experiments based on real-world bus transit datasets confirm that the TSD-ST model shows better performance in prediction tasks at 30-min, 60-min, and 90-min time scales, with an average improvement of 21.87%. The effectiveness of each component has been verified through ablation experiments.

Prediction of short-term passenger flow of subway based on LSTM model

With the continuous development of urban rail transit, the subway is becoming the travel choice for more and more people. And the increase in travel demand also brings about the problem of subway congestion. This paper will use the subway card swipe data of Hangzhou City from January 1st to 20th, 2019, which were counted after data cleaning. With root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R^2), and loss values as standards, the long short-term memorys (LSTM) parameters were adjusted. The LSTM model is trained with time and passenger flow data, and the model is used to predict the short-term passenger flow in and out of subway stations. From the prediction results, the LSTM model has a good adaptability to the subway station short-term passenger flow. The trend of the predicted line is the same as the real value, and the difference is also controllable. The short-term passenger flow prediction of subway stations is beneficial to reduce the loss caused by congestion and improve the travel efficiency of residents.

A Time Series Decomposition and Reinforcement Learning Ensemble Method for Short-Term Passenger Flow Prediction in Urban Rail Transit

Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize the allocation of rail transit resources. However, the nonlinear and nonstationary nature of passenger flow time series challenges STPFP. To address this issue, a hybrid model based on time series decomposition and reinforcement learning ensemble strategies is proposed. Firstly, the improved arithmetic optimization algorithm is constructed by adding sine chaotic mapping, a new dynamic boundary strategy, and adaptive T distribution mutations for optimizing variational mode decomposition (VMD) parameters. Then, the original passenger flow data containing nonlinear and nonstationary irregular changes of noise is decomposed into several intrinsic mode functions (IMFs) by using the optimized VMD technology, which reduces the time-varying complexity of passenger flow time series and improves predictability. Meanwhile, the IMFs are divided into different frequency series by fluctuation-based dispersion entropy, and diverse models are utilized to predict different frequency series. Finally, to avoid the cumulative error caused by the direct superposition of each IMF’s prediction result, reinforcement learning is adopted to ensemble the multiple models to acquire the multistep passenger flow prediction result. Experiments on four subway station passenger flow datasets proved that the prediction performance of the proposed method was better than all benchmark models. The excellent prediction effect of the proposed model has important guiding significance for evaluating the operation status of urban rail transit systems and improving the level of passenger service.

Research on short-term passenger flow prediction method of urban railway based on machine learning

With the development of urban rail transit, the improvement of communication transmission technology, and big data processing level, the passenger flow data of urban rail transit continues to grow and can be effectively collected and stored, providing many basic data resources for analysis and passenger prediction research. With the development of artificial intelligence, short-term passenger flow prediction based on machine learning has received widespread attention. For this reason, based on the passenger flow data of Seoul Metro, this paper has carried out experiments and analysis on the existing models RNN-LSTM and ConvLSTM with better performance, providing theoretical support for short-term passenger flow prediction. The method in this paper solves the problem that a single traditional prediction model based on statistical theory is difficult to deal with large-scale complex data. It can complete the data processing task more accurately and efficiently, and has a good application prospect.

Research on Short-Term Passenger Flow Prediction of LSTM Rail Transit Based on Wavelet Denoising

Urban rail transit offers advantages such as high safety, energy efficiency, and environmental friendliness. With cities rapidly expanding, travelers are increasingly using rail systems, heightening demands for passenger capacity and efficiency while also pressuring these networks. Passenger flow forecasting is an essential part of transportation systems. Short-term passenger flow forecasting for rail transit can estimate future station volumes, providing valuable data to guide operations management and mitigate congestion. This paper investigates short-term forecasting for Suzhou’s Shantang Street station. Shantang Street’s high commercial presence and distinct weekday versus weekend ridership patterns make it an interesting test case, making it a representative subway station. Wavelet denoising and Long Short Term Memory (LSTM) were combined to predict short-term flows, comparing the results to those of standalone LSTM, Support Vector Regression (SVR), Artificial Neural Network (ANN), and Autoregressive Integrated Moving Average Model (ARIMA). This study illustrates that the algorithms adopted exhibit good performance for passenger prediction. The LSTM model with wavelet denoising proved most accurate, demonstrating applicability for short-term rail transit forecasting and practical significance. The research findings can provide fundamental recommendations for implementing appropriate passenger flow control measures at stations and offer effective references for predicting passenger flow and mitigating traffic pressure in various cities.

Short-term passenger flow prediction based on deep learning

With the continuous improvement of urbanization, the problem of urban congestion has become increasingly prominent. Rail transit can greatly alleviate the congestion of ground traffic and is considered as a feasible solution to alleviate urban congestion. However, there are many factors affecting rail transit, among which the number of passengers accounts for the majority. Excessive passenger flow will sharply increase the probability of public safety events in confined space. The continuous development of deep learning has benefited from studies such as convolutional neural networks (CNN), so as to correctly predict short-term traffic. Despite continual model accuracy improvement, prior research has given little regard to the application boundaries of diverse networks in traffic prediction. In this paper, by comparing the results of previous studies, the prediction effect and deficiency of long short-term memory (LSTM), CNN, graph convolutional network (GCN) are discussed. And results reveal that prediction models relying on recurrent neural networks (RNN), such as LSTM, can capture the time dimension of short-term passengers very well, but not the spatial patterns, and model training time is lengthy. The GCN model would account for the spatial dependence of the traffic network, but it has poor performance when constructing the deep network. In general, each of these three models has its own unique aspects. Due to some problems in a single model, the multi-model combination deep learning framework is gradually emerging. In practical application, the complexity of the model, performance effect and application value should be considered comprehensively.

Passenger flow prediction and management method of urban public transport based on SDAE model and improved Bi-LSTM neural network

In the era of big data, the exponentially increasing data volume and emerging technical tools have put forward new requirements for enterprise information management. Therefore, it is of great significance to enhance the core competitiveness of enterprises to explore how big data can empower the innovation of enterprise information management. Intelligent transportation system combines a variety of technologies and applies them to a large-scale transportation management system, so as to make a reasonable dispatch of traffic conditions. Aiming at the problem of the relatively low accuracy of bus passenger flow forecasting with the existing models, a short-term passenger flow prediction model combining Stacked Denoising Auto Encoder (SDAE) and improved bidirectional Long-short Term Memory network (Bi-LSTM) is proposed. First, the SDAE model is used to fill in the missing bus passenger flow data, the characteristics of the bus passenger flow data are effectively utilized, and the data with rich information is used to predict the missing values with high accuracy. Second, Bi-LSTM model combined with attention mechanism is used for short-term bus passenger flow prediction. Considering that the data sequence of bus passenger flow is relatively long and there is a two-way information flow, the BiLSTM neural network is used for prediction tasks, and the influence of key factors is highlighted through attention weights to mine the internal laws of passenger flow data. The experimental results show that the proposed method achieves the lowest prediction error among all the comparison methods in the task of short-term bus passenger flow prediction on the public transportation dataset, with MAE, MRE, and RMSE values of 6.014, 0.052, and 9.874, respectively. These findings confirmed the effectiveness of the new model in the passenger flow prediction field.

Prediction of Inbound and Outbound Passenger Flow in Urban Rail Transit Based on Spatio-Temporal Attention Residual Network

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.

Short-term passenger flow prediction during station closures in subway systems

Passenger flow prediction is critical for subway managers to efficiently organize passenger flow and assign capacity resources. Station closures caused by economic conferences and political events alter the topology of subway networks, resulting in large-scale passenger flows with irregular trends. Thus, predicting subway passenger flow during station closures is a challenging task. In this study, we propose a hybrid model for predicting short-term passenger flow during subway station closures. First, a spatiotemporal ensemble prediction model is used to capture the temporal characteristics of passenger flow at each station and the spatial characteristics of passenger flow among distinct groups of stations. Then, to address the effects of station closures, a support vector regression method is devised to investigate the residual time series from the ensemble model. Finally, a case study of Beijing subway is examined to validate the performance of the proposed model. The results indicate that the proposed model is superior to other state-of-the-art models in terms of accuracy and reliability in estimating the effects of station closures. Furthermore, the residuals from station closures are thoroughly converted into white noise and the proposed model is more interpretable than those in previous studies.

Short-term passenger flow prediction for multi-traffic modes: A Transformer and residual network based multi-task learning method

Managing multiple traffic modes cooperatively is becoming increasingly important owing to the diversity of passenger demands. Short-term passenger flow predictions for multi-traffic modes can be applied to the management of the multi-traffic modes system. However, this is challenging because the spatiotemporal features of multi-traffic modes are complex. Moreover, the passenger flows of the multi-traffic modes differentiated and fluctuated significantly. To address these issues, this study proposes a multitask learning-based model, called Res-Transformer, for short-term inflow prediction of multi-traffic modes. The Res-Transformer consists of two parts: (1) modified Transformer layers comprising the Conv-Transformer layer and the multi-head attention mechanism, which helps extract the spatiotemporal features of multi-traffic modes, and (2) the structure of the residual network, which is utilized to obtain correlations among multi-traffic modes and prevent gradient vanishing and explosion. The proposed model was evaluated using two large-scale real-world datasets from Beijing, China. One was a traffic hub, and the other was a residential area. The results not only demonstrate the effectiveness and robustness of the Res-Transformer but also prove the benefits of considering multi-traffic modes jointly. This study provides critical insights into short-term inflow prediction of the multi-traffic modes system.