Short-term Passenger Flow Research Articles

Short-term passenger flow prediction of urban rail transit based on ARIMA model

Abstract. This paper is about the short-term passenger flow prediction of urban rail transit based on the ARIMA model. Due to the acceleration of urbanization and the increasing density of urban rail transit, more and more people are taking urban rail transit. However, excessive short-time passenger flow can lead to traffic accidents. Therefore, it is very important to predict short-term passenger flow. This paper aims to use the ARIMA model to predict the passenger flow in a short period of time in urban rail transit. A competition data set from Alibaba about the short-time passenger flow of a metro station in Shenzhen was adopted and analyzed by the ARIMA model. The data set contains 1,428 samples, and the time granularity is 10 minutes. After research, it has been found that the ARIMA model can basically predict the short-time passenger flow of the metro with accuracy under the appropriate parameter setting. This also means that the application of the ARIMA model can be extended to other aspects of short-time passenger flow prediction, not just the metro.

A study of short-term passenger flow forecasting for public transportation based on the time series method

Abstract. In order to solve the conflicts between the supply and demand of urban transport, improve the living standard of urban residents, raise the level of urbanization, and promote the sustainable socio-economic development of the whole city, it is necessary to increase the pace of urban transport construction, planning and management in China. And at the same time, its necessary to take various measures to regulate the structure of urban transport and guide urban transport to develop in the direction of taking public transport as the mainstay. The experience of the development of urban transportation in China and abroad also proves that prioritizing the development of public transportation is the fundamental way to solve the problem of urban transportation. Public transport passenger flow data is the basis for optimizing public transport operations and scheduling. Accurate passenger flow forecasts on public transport routes can effectively guide public transport operation decisions, formulate operation scheduling plans and effectively improve the operational efficiency of the public transport system. The following are some of the key factors that can be taken into consideration when making passenger flow forecasts. The main contribution of this article is to propose the use of data mining technology, use bus IC card data and bus GPS data to integrate and organize them, conduct correlation analysis on the data, and on this basis, obtain the main distribution characteristics of bus passenger flow, and provide short-term Passenger flow forecast provides relevant ideas.

A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations

A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations

Analysis of tourist flow prediction model of rural tourism on the edge of big cities in China

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

Short-Term Passenger Flow Prediction Based on a Hybrid Model of Support Vector Machine and Long Short-Term Memory Network

Short-Term Passenger Flow Prediction Based on a Hybrid Model of Support Vector Machine and Long Short-Term Memory Network

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

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.

Decision-making Support Platform and Security Design for Rural Leisure Tour Industry based on SEA Method and SVR Model

An accurate short-term passenger flow forecast of rural tourism can avoid accidents as much as possible. However, the short-term passenger flow of rural leisure tourism shows nonlinear, seasonal, random, and other complex characteristics. Meanwhile, the traditional forecasting methods are often difficult to achieve accurate forecasting. Therefore, this study now used the nonlinear mapping function in the support vector regression to convert the passenger traffic training sample into a high-dimensional feature space and established a linear decision function. Then the influence of periodicity on the prediction effect through seasonal index adjustment was reduced. Finally, event triggering combined with core embedding technology for tamper-proof detection was adopted to improve the security of the platform. The results showed that the minimum absolute error of prediction with improved SVM model was 0.27% compared with traditional model and autoregressive integrated moving average model. After the introduction of the Internet search factor, the traffic prediction result was more accurate, which was 0.0425 smaller than that without the introduction of the Internet search factor. When the concurrency was less than 100 times/s, the average response time difference before and after adding the core embedded program was small, indicating that the security of page tampering technology was high. This research method can effectively predict the passenger flow of rural leisure tourism industry and ensure the safety of the platform.

The HWAM-EMD-GRU Forecasting Model for Short-Term Passenger Flow in an Airport Light Rail Transit Line

Accurate forecasting of airport light rail transit line (ALRTL) outbound passenger flow is critical to the optimal operations of both light rail and airport systems. Considering the nonlinearity, non-stationarity, uncertainty, and periodicity of outbound passenger flow in the ALRTL, we propose a combined forecasting model that integrates the Holt and Winters additive model (HWAM), empirical mode decomposition (EMD) and gated recurrent unit (GRU). Firstly, the edge effect of EMD will greatly affect the performance of the forecasting model. To overcome this, we extend the passenger flow by HWAM. After that, the decomposition method, EMD, can be applied to passenger flow, and several intrinsic mode function (IMF) components can be extracted. After extracting all the IMFs, the remaining part is referred to as the residual (Res) component. Then, a correlation test is performed on all the components, followed by their aggregation. Finally, the GRU is used to predict each of the aggregated components, and the prediction of aggregated components requires reconstruction. To verify the performance of the HWAM-EMD-GRU, we conducted a comparative study on the hourly passenger flow data for Beijing Daxing International Airport Express and set the autoregressive integrated moving average model, HWAM, Prophet, and GRU as the baseline. Predictions of the HWAM-EMD-GRU combined model demonstrated higher accuracy than baseline models, with a root mean square error of 83.52 (Prophet is 110.21) and mean absolute percentage error of 8.32% (Prophet is 12.48 %). The experimental result shows that the HWAM-EMD-GRU forecasting model offers more accurate predictions.

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

A Metro Passenger Flow Forecasting Model Based On Time-series Evolving Interaction Graph Network

Passenger flow forecasting is an important task in metro operation management. In order to achieve more accurate metro passenger flow forecasting, this paper proposes a metro passenger flow forecasting model based on time-series evolution interaction graph. First, by introducing two kinds of inter-station interaction graphs, namely connectivity graph and temporal correlation graph, to capture the potential interaction relationship among metro passenger flow stations. Then, by using the time-series evolving graph, the weights of the graph convolutional neural network are dynamically evolved in time series. Finally, taking Suzhou Metro as an example, the short-term passenger flow of the metro is forecasted. The experimental results show that the Root Mean Squared Error (RMSE) of this model is 34.17, the Mean Absolute Error (MAE) is 16.35, the R-Squared is 0.94, the Mean Absolute Percent Error (MAPE) is 0.21. All evaluation metrics are better than the baseline models, thus verifying the effectiveness and applicability of the metro passenger flow forecasting model based on the time series evolution interaction graph.

Combining travel behavior in metro passenger flow prediction: A smart explainable Stacking-Catboost algorithm

Accurately predicting short-term passenger flow is essential to optimize operation resources and improve transportation services in urban metro systems. However, it has become a challenging problem due to spatial-temporal demand fluctuations and heterogeneous passengers’ travel behavior, i.e., the interaction between the departure and arrival passengers. In this paper, we develop an explainable Stacking-Catboost model for passenger flow prediction combining the passenger’s return probability computation. The model explores several basic ensemble learning models and the best stacking strategy. To better characterize the macroscopic spatiotemporal travel patterns other than the micro individual travel behavior, several relevant variables such as train operation characteristics, the nearby bus stations, and points of interest are considered. Ablation studies are conducted to investigate the utility of each component of the proposed model. An explainable analysis is performed to interpret information from “black box” models and quantify the contribution of each feature. We present a real-world case study conducted in the Beijing metro, demonstrating that our proposed method achieves significant improvements over existing techniques in hourly prediction tasks. Specifically, our approach outperforms the CategoricalBoosting (CatBoost) by up to 11.11 % and the Random Forest (RF) by up to 12.90 %, showcasing the effectiveness of incorporating macroscopic spatiotemporal travel patterns and micro individual travel behavior to enhance short-term forecasting accuracy. The global interpretability of models reveals key factors that impact performance, such as returning passenger flow, historical travel demand, and timetable data. Our results offer valuable insights into short-term forecasting challenges and provide a framework for leveraging explainable artificial intelligence to improve transportation services in urban metro 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.

Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station

Accurate understanding of passenger flow distribution is crucial for effective station crowd management. However, due to the complexity and randomness of passenger flow and the unclear spatial-temporal correlation between functional areas within the station, predicting the spatiotemporal distribution dynamics of inflow and future short-term distribution trends is challenging. Emerging deep learning models offer valuable insights for accurately predicting passenger flow distribution. Thus, we propose a deep learning architecture, named “ST-Bi-LSTM,” which combines a bidirectional long short-term memory network with a spatial-temporal attention mechanism. Initially, we outline the methodologies of Bi-LSTM, the DeepWalk-based spatial attention mechanism, and the temporal attention mechanism. The spatial attention mechanism is employed to extract station spatial network topology information and enhance the representation of passenger flow characteristics in highly correlated areas during the forecasting process. Simultaneously, the temporal attention Bi-LSTM is utilized for capturing temporal correlations. The architecture comprises four branches dedicated to station real-time video monitoring data, spatial network topology, function area attributes, and train timetables. Subsequently, leveraging in-station CCTV data, passenger travel behavior data, and train timetables, we apply the architecture to the Tianjin West High-Speed Railway Station. We conduct a comparative analysis of the prediction performance and time complexity of the proposed architecture against existing baseline models, demonstrating superior performance and robustness exhibited by the ST-Bi-LSTM model (achieving a reduction in RMSE of over 10%). This study facilitates the transition of station management from passive response to active prediction of station passenger flow dynamics.

Multi-Stage Fusion Framework for Short-Term Passenger Flow Forecasting in Urban Rail Transit Systems Using Multi-Source Data

To improve real-time operation and management in urban rail transit (URT) systems, accurate and reliable short-term passenger flow forecasting at the network level is a crucial task. Although numerous endeavors have been devoted to this field, the insufficient topological representation for passenger flows in the URT network, the overlooking of intrinsic correlations among multi-source data, and the information loss in deep-learning frameworks are still critical issues that need to be addressed. This study proposes a multi-stage fusion passenger forecasting (MSFPF) model to accomplish short-term multi-step passenger forecasting leveraging multi-source data, and overcome the above-mentioned challenges. Based on the characteristics of passenger flows in the URT network, time-based origin–destination flow data is involved and utilized to enhance the representation of flows and provide spatial-temporal features. Then, the interaction and relationship among multi-source data are estimated to capture their intrinsic correlations. To effectively and comprehensively extract temporal and spatial features, a transformer long short-term memory block and a depth-wise attention block are constructed with attention mechanisms and employed. Furthermore, we construct the multi-stage fusion (MSF) structure to alleviate the information loss during the learning process, which is a significant component in improving the forecasting accuracy. In addition, the model is applied to two large-scale real-world datasets, in which it outperforms nine widely used baselines and four specific variants of itself. The quantitative experiments demonstrate the robustness and superiority of the proposed MSFPF model, and the significant contribution of the MSF structure in the model.

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

Optimization of Short-Term Passenger Flow Forecast Based on Improved Sparrow Search Algorithm in LSTM Network

Optimization of Short-Term Passenger Flow Forecast Based on Improved Sparrow Search Algorithm in LSTM Network

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