Subway Passenger Flow Research Articles

An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environment

The COVID-19 pandemic and similar public health emergencies have significantly impacted global travel patterns. Analyzing the recovery characteristics of subway station-level passenger flow during the pandemic recovery phase can offer unique insights into public transportation operations and guide practical planning efforts. This pioneering study constructs a station-level passenger flow recovery resilience (PFRR) index during the rapid recovery phase using subway AFC system swipe data. Additionally, it develops an analytical framework based on a multiscale geographically weighted regression (MGWR) model, the improved gray wolf optimization with Levy flight (LGWO), and light gradient boosting machine (LightGBM) regression to analyze passenger flow resilience on weekdays and weekends in relation to land use-related built environment types. Finally, SHAP attribution analysis is used to study the nonlinear relationships between built environment variables and PFRR index. The results show significant spatial heterogeneity in the impact of commercial, recreational, and residential land, as well as POI (points of interest) of leisure and shopping on PFRR. On weekdays, the most relevant built environment variables for PFRR are POI of enterprises and shopping numbers. In contrast, the contribution of built environment variables affecting PFRR of weekend is more balanced, reflecting the recovery of non-essential travel on weekends. Most land use-related built environment variables exhibit nonlinear associations with PFRR values. The proposed analytical framework shows significant performance advantages over other baseline models. This study provides unique insights into subway passenger flow characteristics and surrounding land use-related development layouts under the impact of public health emergencies.

Enhancing periodic mobility planning: ARIMA-driven prognostications for subway passenger volume dynamics

Abstract. This study aims to accurately predict urban subway passenger flow using the Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto Regressive Integrated Moving Average (SARIMA) model, with the intention of providing a scientific basis for subway operation management. By meticulously sorting and analyzing the existing traffic data of a large city's subway system, this research constructs a model that comprehensively considering the seasonal, trend, and stochastic characteristics of time series data. During the model construction process, data preprocessing is carried out, including stationarity tests, differencing, model order determination, and parameter estimation, ultimately identifying an ARIMA and SARIMA model with good fitting results, effectively identifying and forecasting characteristics such as numerical fluctuations. Additionally, this study conducts a special analysis of the prediction effects at diverse time scales, verifying the applicability and superiority of the ARIMA and SARIMA model in subway passenger flow prediction. This research not only provides a powerful tool for passenger flow prediction for subway operation management departments, aiding in the reasonable allocation of capacity and optimization of operation strategies, but also holds significant reference and application value for the passenger flow prediction and management of other urban public transportation systems.

Built Environment’s Non-Linear Impact on Subway Passenger Flow Through Improved Interpretable Machine Learning

Understanding the complex correlation between the built environment and subway passenger flow can provide unique insights for the development of transportation operations and urban coordination policies. Few studies have systematically analyzed the rationality of selecting built environment variables and further explored the non-linear relationships. In this study, we integrated various sources of built environmental factors and developed an interpretable machine learning analysis framework using backward elimination extreme gradient boosting and SHapley Additive exPlanations (SHAP) values analysis (BE-XGBoost-SHAP). The framework was validated by analyzing passenger flows during the morning peak, non-peak, and evening peak periods at the station level. The research results indicate that there are significant differences between built environment factors and the time-varying passenger flow. Land use characteristics significantly dominate across all three temporal periods. The importance of other variable types in relation to passenger flows varies significantly across the three time periods. It is worth noting that the relationships between all variables and passenger flow at different time periods are non-linear, with the majority displaying threshold effects. Compared with the gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models, the proposed interpretive framework performs better as regards R-square, root mean square error (RMSE), and mean absolute error (MAE) metrics. This study offers valuable insights, elucidating the pivotal land use attributes that notably affect passenger flow, the significance of varied built environment factors across distinct time spans, and the acknowledgment of non-linearities and threshold effects within these relationships. These findings are imperative for urban planning and the enhancement of station area design.

Forecasting short-term subway passenger flow using Wi-Fi data: comparative analysis of advanced time-series methods

Accurately monitoring passenger demand fluctuations is crucial for streamlined operations of subway systems and informed decision-making. This study presents a detailed Time Series Analysis of the Toronto subway system using Wi-Fi data connection from devices as a predictor of passenger volume. Various time series models were tested for short-term forecasting, including Linear Regression, Exponential Smoothing, ARIMA, Random Forest, N-BEATS, and T-GCN. An end-to-end modeling implementation process was carried out, and the performance of each model was evaluated. The primary objective was to assess the effectiveness of short-term prediction models for univariate time series at the system level and discuss deployment challenges. While conventional time series models are fast to implement and interpretable, they require a more in-depth data exploration phase for validation, making scaling at the system level difficult. Additionally, maintenance is more challenging with conventional models, and their exploratory analysis phases need to be repeated when the models degrade over time. Prediction difficulty varied across each subway station, indicating the need for a more thorough calibration or hybrid approach, especially for transfer stations. Despite the different uses and qualities of each model in our scenario, Random Forest and Exponential Smoothing emerged as the best performers and could be a satisfactory option for robust demand forecasting at the system level.

Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling

Tensor clustering has become an important topic, specifically in spatiotemporal modeling, because of its ability to cluster spatial modes (e.g., stations or road segments) and temporal modes (e.g., time of day or day of the week). Our motivating example is from subway passenger flow modeling, where similarities between stations are commonly found. However, the challenges lie in the innate high-dimensionality of tensors and also the potential existence of anomalies. This is because the three tasks, that is, dimension reduction, clustering, and anomaly decomposition, are intercorrelated with each other, and treating them in a separate manner will render a suboptimal performance. Thus, in this work, we design a tensor-based subspace clustering and anomaly decomposition technique for simultaneous outlier-robust dimension reduction and clustering for high-dimensional tensors. To achieve this, a novel low-rank robust subspace clustering decomposition model is proposed by combining Tucker decomposition, sparse anomaly decomposition, and subspace clustering. An effective algorithm based on Block Coordinate Descent is proposed to update the parameters. Prudent experiments prove the effectiveness of the proposed framework via the simulation study, with a gain of +25% clustering accuracy over benchmark methods in a hard case. The interrelations of the three tasks are also analyzed via ablation studies, validating the interrelation assumption. Moreover, a case study in station clustering based on real passenger flow data is conducted, with quite valuable insights discovered. History: Bianca Maria Colosimo served as the senior editor for this article. Funding: Dr. Yan is partially funded by DOE [DE-EE0009354] and NSF [CMMI 2316654]. Dr. Fugee is partially funded with the RGC [GRF 16201718 and 16216119]. The authors appreciate the help from the Hong Kong MTR Co. research, marketing, and customer service teams. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/6536164/tree and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.0028 ).

Point and interval forecasting approach for short-term urban subway passenger flow based on residual term decomposition and fuzzy information granulation

Accurate forecasting information of short-term subway passenger flow is an important scientific reference for daily operations and urban management. The rapid time-varying nature of subway passenger flow caused by various factors that affect travel behavior poses a huge challenge to accurate forecasting. The complexity and uncertainty of data mainly focus on residual terms that deeply reflect the fluctuations. Reasonably mining the residual terms has become the key to achieving accurate forecasting. Therefore, this paper proposes a sophisticated decomposition strategy to extract and analyze the useful information hidden in the residual terms. Firstly, the potential trend, seasonal and residual terms from the original data are extracted by seasonal-trend decomposition procedure based on loess. Secondly, the residual term is decomposed into a series of subcomponents with different frequency features by symplectic geometric mode decomposition. Thirdly, these subcomponents are classified into three clusters based on fuzzy C-means clustering (FCM), and then the corresponding forecasting models are matched to the three obtained clusters and trend and seasonal terms. Finally, based on a decomposition-ensemble framework and information granulation for high-frequency components, we have established point and interval forecasting approach, respectively. Three experiments on real data sets in Beijing, Guangzhou and Shenzhen are conducted to verify the performance of the proposed approach, and the experimental results show that our approach is superior to all benchmark models and contributes to improving operational management and service quality.

Short-term subway passenger flow forecasting approach based on multi-source data fusion

Accurate short-term subway passenger flow forecasting is essential in improving the efficiency of daily operation and the management of subway system. Existing research often overlooks the impacts of randomness and unevenness in passenger flows caused by population migrations, also predominantly emphasizes feature selection while neglecting effective feature fusion. To address this gap, we propose a novel hybrid subway passenger flow forecasting approach adopting multi-source data fusion technique. Firstly, the dual processing module consisting of Grey Relation Analysis and SHapley Additive exPlanations is used to gather influencing features. Then, the multivariate empirical mode decomposition decomposes the selected features into multidimensional intrinsic mode functions. To discern and extract the effective components from the decomposed intrinsic mode functions, secondary feature selection module based on Spearman Correlation Coefficient is performed. Finally, the determined components are categorized into three classes by combining recurrence plot and picture information entropy, each class is assigned suitable predictor to ensure accurate forecasting. Experimental results based on three real subway passenger flow datasets from Guangzhou, Beijing, and Chengdu verifies the effectiveness and robustness of the proposed approach. Therefore, the introduction of population migration index and the effective fusion of information are significant to improve subway passenger flow forecasting.

Non-linear impact of the built environment on metro commuter flows before and after the COVID-19 outbreak: A case study in Guangzhou

The built environment is a crucial factor influencing subway commuting. However, the extent to which the built environment affects metro travel during the COVID-19 pandemic remains under explored. In particular, people's travel patterns and destinations might have changed during the pandemic, influenced by the built environment. The degree to which these influences differ from those in normal circumstances has scarcely been studied. This study investigates the non-linear effects of the built environment on metro commuters within an 800-m radius of subway stations. Analysis of mobile signal data from Guangzhou Metro Line 1 showed that the importance of the built environment was 81.69% and 70.06% before and after the outbreak, respectively. Based on the threshold effects of the built environment on subway passenger flows before and after the pandemic, this study determined the immediate needs of commuters for the built environment after the outbreak. This information can guide planning strategies around subway stations facing public health emergencies. This study highlights the non-linear impact of the built environment on subway passenger flows after an outbreak. Thus, the number of parking lots, the proportion of medical facilities and the distance to the central business district are urgent demands for subway passengers after the outbreak.

Intelligent Metro Passenger Flow Monitoring and Guidance System

With the rapid development of big data, image analysis technology, and artificial intelligence, the related businesses of smart subways have been gradually realized. Facing the huge passenger flow during peak hours, how to guide passengers to relatively spacious compartments has become a hot issue of concern. This article utilizes big data processing and existing image analysis technology in combination with the original subway system to develop an intelligent subway passenger flow monitoring and guidance system. This system consists of three parts: multi-terminal collection, data analysis, and intuitive guidance. The system has the advantages of high reliability, low cost, and real-time guidance, which is expected to help improve passengers' ride experience and ensure the normal operation of the subway.

Front-end speech processing system with SVM algorithm in rail passenger flow management

In order to achieve the goal of dynamically adjusting daily passenger flow to effectively control the overall efficiency of the transportation system, this study constructs a real-time monitoring and prediction system for subway passenger flow based on front-end voice processing technology and support vector machine models. The study first conducted a railway passenger flow analysis, and then used a support vector machine model to construct a preliminary prediction system. In order to achieve global optimization, the study also introduced particle swarm optimization algorithm to construct an optimization prediction model based on PSO-SVM. The results show that the proposed PSO-SVM method has undergone 48 iterations of training, and the predicted values closely match the actual passenger flow curve. The maximum RE error is 2%, and the overall prediction error is 98%. The decision coefficient of PSO-SVM is 0.998932. Therefore, this indicates that it has high performance and feasibility in predicting and controlling passenger flow during peak hours of urban rail transit.

SAD-ARGRU: A Metro Passenger Flow Prediction Model for Graph Residual Gated Recurrent Networks

This paper proposes a graph residual gated recurrent network subway passenger flow prediction model considering the flat-peak characteristics, which firstly proposes the use of an adaptive density clustering method, which is capable of dynamically dividing the flat-peak time period of subway passenger flow. Secondly, this paper proposes graph residual gated recurrent network, which uses a graph convolutional network fused with a residual network and combined with a gated recurrent network, to simultaneously learn the temporal and spatial characteristics of passenger flow. Finally, this paper proposes to use the spatial attention mechanism to learn the spatial features around the subway stations, construct the spatial local feature components, and fully learn the spatial features around the stations to realize the local quantization of the spatial features around the subway stations. The experimental results show that the graph residual gated recurrent network considering the flat-peak characteristics can effectively improve the prediction performance of the model, and the method proposed in this paper has the highest prediction accuracy when compared with the traditional prediction model.

Analysis of Influencing Factors of Beijing Subway Passenger Flow Based on Data Analysis Method

Subway passenger flow volume is a hot issue of concern to society. Some researchers develop predictive models to analyze short-time flow volume based on previous data. However, predictive ways to estimate the number of passenger flows before the subway construction still need to be completed. Therefore, by focusing on Beijing Subway Line 10, the article analyzes the factors influencing the passenger flow volume based on data analysis methods. The research finds that passenger flow volume on weekdays is greater than on weekends; as the number of stations and transfer stations increases, the volume increases; monofunctional stations have higher passenger flow volume. Before the construction of the subway, the design can be more appropriate according to relative factors. As a result, it is beneficial to release the stress of management and issues happening while the subway is running, decreasing operating costs and risks. In future studies, more detailed analyses in particular cases can be done.

Logistic model for pattern inference of subway passenger flows based on fare collection and vehicle location data

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods.

Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events

Accurate forecasting of subway passenger flows is considered essential for the development of efficient train schedules. However, transport capacity constraints as well as station congestion can be caused by unexpected concerns with trains or power supply, which endanger passenger safety. Predicting passenger flows at the time of a fault is particularly challenging due to the low probability of failure and the complexity of the factors involved. In addition, deviation from the observed value may be resulted by the point-in-time prediction of passenger flow, thus affecting the efficiency of passenger flow control measures. To address this concern, a three-stage A-LSTM prediction model utilizing an attention mechanism and a double-layer LSTM (Long Short-Term Memory) neural network has been proposed. The model is used to map the impact of fault events on subway transport capacity with respect to delays onto the inbound passenger flow. By analyzing the data from the subway system in a metropolitan city of China, the range of passenger flow fluctuations in 10-minute intervals will be precisely predicted and applied to different subway stations.

Medium-Term Forecast of Metro based on Big Traffic Data

With the rise of industrialization and automation, the demand for transportation has been continuously increasing. The demands for quick and effective travel can no longer be met by conventional transportation techniques. In the field of transportation, urban train travel is significant. At the same time, passenger flow prediction has become increasingly important. In addition to short-term forecasts, medium-term forecasts of daily intervals of passenger flow are also essential. This research employs the ARIMA model to forecast medium-term passenger flow, utilizing subway passenger flow data collected in Chengdu City from April 29th to September 28th, 2019. Based on the data and methodology, this study investigates the feasibility of using the ARIMA model for medium-term subway passenger flow prediction. The research findings indicate that the model exhibits strong seasonality, with an ADF unit root test p-value less than 0.05 and an LB statistic test p-value less than 0.05. The Mean Percentage Error is only -1.03%. Furthermore, there is a strong concordance between the fluctuation patterns observed in the actual and predicted values of the model, which closely align with the trends observed in the training dataset. Through this research, it is concluded that the ARIMA model performs well in medium-term subway passenger flow prediction.

Short-time subway passenger flow forecast based on the ARIMA-LSTM

In recent years, with the rapid development of Chinas economy and the continuous increase in population, the urbanization process has intensified. This travel issue caused by peak traffic congestion affects a significant portion of urban populations. In order to conduct a thorough analysis, this article primarily focuses on the urban rail transit industrys subway system. In this study, three models - LSTM, ARIMA and ARIMA-LSTM were employed to analyze and predict short-term inbound flow data for the Hangzhou subway in January 2019. While the LSTM model used for forecasting, it was found to be ineffective in predicting data with extreme peaks. The ARIMA model is subsequently employed for data prediction, revealing its inadequacy in accurately forecasting unstable and non-patterned data. To overcome this limitation, a combined ARIMA-LSTM model is proposed to mitigate the shortcomings of individual models and achieve superior performance. The implementation of the ARIMA-LSTM model effectively mitigates subway congestion issues, thereby facilitating informed decision-making by subway operators. Moreover, it has a certain degree of robustness, even if the other data are not regular enough. The model can be applied in the real subway passenger flow prediction, which is conducive to the choice of people traveling to work and the management decisions of subway operators.

Multi-Step Subway Passenger Flow Prediction under Large Events Using Website Data

Multi-Step Subway Passenger Flow Prediction under Large Events Using Website Data

Research on the Influence of Weather Factors on Urban Rail Transit Passenger Flow

In recent years, cities have been increasingly confronted with frequent and severe extreme weather events, such as heavy rainfall, high temperatures, and strong winds. These extreme weather conditions not only impact urban transportation operations but also directly affect passengers' travel patterns and behaviors. In this context, gaining a deeper understanding of the mechanisms through which weather factors influence urban subway passenger flow becomes crucial. This study aims to investigate the relationship between passenger flow and weather factors, and provide valuable insights for urban rail transit management to enhance proactive decision-making. To achieve this research objective, we first establish a framework for studying the impact of weather on passenger flow. Based on this framework, an analysis is conducted using passenger flow data and weather data from Beijing Metro Line 4. The findings of this study reveal significant effects of different weather factors on subway passenger flow. Increasing severity of weather conditions and higher wind speeds have a negative influence on passenger flow, leading to a decrease in ridership. Conversely, a significant positive correlation is observed between the highest temperature and subway passenger flow, indicating that as the highest temperature rises, the passenger volume also increases.

A tensorial weighted Schatten-[formula omitted] norm model with neighbor regularization for traffic data completion and traffic system correlation exploration

Traffic data exhibit the spatiotemporal heterogeneity. Revealing traffic system correlations (TSC) from traffic data, which refers to discovering inter-station correlations incurred from movements of passengers, is a meaningful task for releasing traffic congestion and sustainable urban operations. Since traffic data involve a great loss or corruption due to equipment and transmission failures, traffic data completion (TDC) serves as a necessary task for TSC exploration. We observe that: for the TDC task, traffic data exhibit anisotropic periodicity and repetition along different temporal dimensions, which cannot be captured by traditional low-rank TDC methods; for the TSC exploration task, a traffic network obeys graphical structure and exhibits a strong spatial correlation between stations or road segments. Based on these observations, we propose a Tensorial Weighted Schatten-pNorm model with Neighbor Regularization (TWSN2R) for accomplishing the two tasks simultaneously. TWSN2R consists of a tensorial weighted Schatten-p norm term and a neighbor regularization term. The first term mines the anisotropic periodicity with a tensor form of weighted Schatten-p norm which preserves suitable rank components of traffic data by allocating different weights in both tensorial dimension and decomposed mode dimension. The second term mines a spatial correlation of a traffic network by enforcing a neighbor contribution of passenger flows incurred from the network, and extrinsically reveals an inter-station (or inter-road-segment) TSC of the network. Experiments on the subway flow dataset MetroBJ and the road network vehicle speed dataset RoadBJ demonstrate that, TWSN2R not only outperforms several state-of-the-art TDC methods, but also reveals two pieces of regulations of TSC. (a) For subway passenger flows, positive correlated stations and negative correlated stations contribute to passenger flows differently according to each station’s attributes. (b) For road networks, urban loops, expressways and transportation hubs contribute to the vehicle flows at a majority, and serve as the critical roads for mitigating the congestion of whole road networks.

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.