Subway Passengers Research Articles

Influence Analysis of Field Evacuation in Reducing Psychological Anxiety of Subway Fire

In order to effectively reduce the psychological anxiety of subway passengers in the fire state and improve the effect of fire emergency management, the fire anxiety status of passengers of different genders was counted, based on the fire state-trait anxiety questionnaire. The questionnaire and mathematical statistics were used to discuss the influence of different time periods after the intervention of on-site drills on the psychological anxiety level of passengers. Results: Before the field exercise, the overall fire anxiety level of the sample was in the normal range ; After on-site exercise, female fire anxiety level decreased, but still did not eliminate anxiety, and the effect of male education is not obvious ; After one hour of on-site training, the overall scores of fire anxiety of the samples were reduced; One day after the on-site exercise, women’s psychological anxiety scores began to improve, and men’s psychological anxiety continued to improve. Conclusion: On the whole, on-site drills are effective. After one day of on-site drills, the intervention effect basically disappears. However, on-site drills have a significant effect on eliminating the tension in the face of subway fires, and there are significant differences in these aspects of characteristics between people of different genders in the face of subway fires.

COVID-19 Confirmed Cases and Subway Passengers

COVID-19 Confirmed Cases and Subway Passengers

Development of an automated software tool based on machine learning methods for solving problems of radio planning in subway sections

Introduction: The modern approach to radio planning provides subway passengers with uninterrupted access to the Internet. This is achieved through the use of a special signal propagation model which calculates signal power loss during its propagation between a transmitter and a receiver on subway lines. The disadvantage of the model is the high computational complexity. Purpose: Using machine learning methods to develop an algorithm for predicting the signal power loss, the algorithm being characterized by high accuracy and low computational complexity. Results: The analysis of machine learning methods revealed that the maximum possible accuracy in solving the problem is provided by the random forest method. A data structure containing the parameters of a digital map of subway lines was developed to train the selected method and predict a signal power loss. While developing the final algorithm a number of assumptions were made, such as: the problem is solved as a classification problem, the predicted values are integers. A signal power loss prediction algorithm that does not directly use the propagation model was developed, which reduced the computational complexity and the execution time for solving radio planning problems, with high prediction accuracy maintained. Practical relevance: Due to the use of machine learning methods in developed algorithms the time for performing radio planning was reduced from several days to several hours, with accuracy preserved. This allows to process more radio planning orders or to reduce the working time for engineers to complete the same number of orders, which is a financial benefit.

Hybrid models of subway passenger flow prediction based on convolutional neural network

AbstractAccurate and stable short‐term passenger flow prediction is an indispensable part of current intelligent transportation systems. This paper proposes two deep learning prediction models based on convolutional neural networks (CNN) and long short‐term memory neural network (LSTM). Combining the CNN characteristics and the LSTM, the ConvXD‐LSTM extracts passenger flow features through CNN and then inputs the time series into the LSTM. The ConvLSTM converts the weight calculation of the LSTM into convolution operation to realize short‐term passenger flow prediction. Fuzhou Metro Line 1 passenger flow data was used for verification. The models were used to predict the passenger flow of subway stations and cross‐sections and compared with the traditional prediction models. In terms of prediction accuracy, ConvLSTM has the highest accuracy, followed by ConvXD‐LSTM. In terms of running time, ConvXD is the fastest and LSTM takes the longest. ConvXD‐LSTM and ConvLSTM are in the middle of the two models, achieving a good balance between accuracy and efficiency. Compared with ConvXD‐LSTM, ConvLSTM has a relatively simple network structure, which reduces the computational burden and improves the prediction accuracy.

Recent developments in evaluation methods and characteristics of comfort environment in underground subway

In recent years, due to the rapid progress of urbanization, the subway system with the advantages of large transport capacity, punctuality, efficiency, convenience and safety has become one of the main transportation modes in metropolitan areas. With the increase in passenger flow, the comfort of subway passengers has attracted extensive attention from the academic community. In this paper, we begin by analyzing the characteristics of the subway environment and sort out six environmental elements that affect passengers’ comfort, including thermal environment, vibration, noise, lighting, air quality, and air pressure. In addition, the measurement scheme, calculation model, and evaluation method of each element are outlined based on relevant norms and literature. Through reviewing the studies in the past 2 decades, it is found that the in-depth research is still in demand for a comprehensive comfort evaluation model with multi-element coupling. A deep understanding of the subway passengers’ comfort is the basis for the design, development, and operation regulation of the subway environmental control system. Measures to improve comfort, especially the exploitation of energy-saving air conditioning systems, will provide strong support for the sustainable and sound growth of the rail transit industry.

GMM Clustering Based on WOA Optimization and Space-Time Coupled Urban Rail Traffic Flow Prediction by CEEMD-SE-BiGRU-AM

Accurately predicting the short-term passenger flow of urban subways is very important for urban subway stations to formulate passenger flow organization and evacuation plans effectively and rationally plan passenger travel routes. This paper establishes a novel framework to predict the hourly inbound and outbound passenger flow of subway stations based on WOA-GMM station classifiers, CEEMD-SE noise reduction, and BiGRU optimized by attention. Firstly, this paper classifies subway stations using the improved Gaussian mixture model (GMM) with Whale Optimization Algorithm (WOA) to realize the feature extraction of different types of subway stations. Secondly, this paper uses the Complementary Ensemble Empirical Mode Decomposition (CEEMD) to decompose the noise reduction of each station’s hourly inbound and outbound passenger flow. It combines the empirical modal components by calculating the sample entropy (SE), which makes the time series stable and reduces the time cost of forecasting. Finally, a Bidirectional Gated Recurrent Unit (BiGRU) model improved by attention mechanism (AM) is established for each station’s inbound and outbound passenger flow. The prediction model established in this paper is verified by subway passenger flow data in Shanghai, China, within 24 days. Finally, it is concluded that the model can predict the passenger flow of subway stations. Compared with the traditional Backpropagation Neural Network (BP), the Long Short-Term Memory (LSTM), and the normal BiGRU model, the model proposed in this paper has an average reduction of 65.90%, 64.54%, and 49.06% in Mean Absolute Percentage Error (MAPE) in the prediction of the hourly inbound and outbound passenger flow of each type of station, respectively.

Sixteen-Year Monitoring of Particulate Matter Exposure in the Parisian Subway: Data Inventory and Compilation in a Database

The regularly reported associations between particulate matter (PM) exposure, and morbidity and mortality due to respiratory, cardiovascular, cancer, and metabolic diseases have led to the reduction in recommended outdoor PM10 and PM2.5 exposure limits. However, indoor PM10 and PM2.5 concentrations in subway systems in many cities are often higher than outdoor concentrations. The effects of these exposures on subway workers and passengers are not well known, mainly because of the challenges in exposure assessment and the lack of longitudinal studies combining comprehensive exposure and health surveillance. To fulfill this gap, we made an inventory of the PM measurement campaigns conducted in the Parisian subway since 2004. We identified 5856 PM2.5 and 18,148 PM10 results from both personal and stationary air sample measurements that we centralized in a database along with contextual information of each measurement. This database has extensive coverage of the subway network and will enable descriptive and analytical studies of indoor PM exposure in the Parisian subway and its potential effects on human health.

Forecasting Subway Passenger Flow for Station-Level Service Supply.

Demand forecasting is one of the managers' concerns in service supply chain management. With accurate passenger flow forecasting, the station-level service suppliers can make better service plans accordingly. However, the existing forecasting model cannot identify the different future passenger flow at different types of stations. As a result, the service suppliers cannot make service plans according to the demands of different stations. In this article, we propose a deep learning architecture called DeepSPF (Deep Learning for Subway Passenger Forecasting) to predict subway passenger flow considering the different functional types of stations. We also propose the sliding long short-term memory (LSTM) neural networks as an important component of our model, combining LSTM and one-dimensional convolution. In the experiments of the Beijing subway, DeepSPF outperforms the baseline models in three-time granularities (10, 15, and 30 minutes). Moreover, a comparison between variants of DeepSPF indicates that, with the information of stations' functional types, DeepSPF has strong robustness when an abnormal situation happens.

A Comprehensive Spatio-Temporal Model for Subway Passenger Flow Prediction

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.

Understanding individual and collective human mobility patterns in twelve crowding events occurred in Shenzhen

Crowding events, which pose tremendous pressure to city management and society safety, are a typical manifestation of anomalous human mobility in metropolitan areas. However, we are still lacking a comprehensive understanding of the anomalous human mobility in crowding events, which is crucial for preventing crowd disasters and developing sustainable cities and societies. In this study, we analyze the individual and collective human mobility patterns in crowding events using the smart card data of six million subway passengers in Shenzhen city. The discovered individual human mobility patterns reveal the underlying mechanism of crowd formation. The discovered collective human mobility patterns can be employed to anticipate crowding events, offering timely information for transportation and crowd management. We analyze the human mobility patterns in twelve crowding events. Individual human mobility patterns reveal the mechanism of crowd formation. Collective human mobility patterns are used to anticipate crowding events.

A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events

Predicting individual mobility of subway passengers in large crowding events is crucial for subway safety management and crowd control. However, most previous models focused on individual mobility prediction under ordinary conditions. Here, we develop a passenger mobility prediction model, which is also applicable to large crowding events. The developed model includes the trip-making prediction part and the trip attribute prediction part. For trip-making prediction, we develop a regularized logistic regression model that employs the proposed individual and cumulative mobility features, the number of potential trips, and the trip generation index. For trip attribute prediction, we develop an n -gram model incorporating a new feature, the trip attraction index, for each cluster of subway passengers. The incorporation of the three new features and the clustering of passengers considerably improves the accuracy of passenger mobility prediction, especially in large crowding events.

Founding AP's IVM and related IHM for subway LiFi network

In this paper, the information value model (IVM) of an access point (AP) and the related intelligent handover model (IHM) are firstly founded in the subway light-fidelity (LiFi) network. At first, based on statistical methods, the IVM is created by analyzing the behavioral characteristics of subway passengers. By the IVM, the probability of user access to each AP can be successfully predicted. Next, based on this probability, the IHM is created by deeply analyzing the alternating blockage problem of handholds. By the IHM, the system can self-adapt to the hysteresis state. This not only effectively avoids ping-pong handovers, but also reduces outage probability. Finally, by simulation, the effectiveness of the above IVM and IHM has been demonstrated. The simulation results indicate that the accuracy of predicting user target AP can be up to 92% by the IVM. Specially, compared with using the standard handover model, the throughput of the network not only increases 11.5%, but also the outage probability of the network decreases 23.6% by using the IHM.

Knowledge Graph-Based Enhanced Transformer for Metro Individual Travel Destination Prediction

Accurate and timely destination prediction of subway passengers is of great significance in improving urban residents’ travel efficiency, alleviating urban traffic pressure, and recommending the proper location-based service. Although some individual travel destination prediction methods have been proposed, the prediction performance is poor due to the large difference in travel locations of different individuals, the difficulty of evaluating the individual travel intention, the sparsity of individual travel trajectory data, and other problems. To solve these problems, this paper proposes a knowledge graph-based enhanced Transformer method (KG-Trans) for the metro individual travel destination prediction task (MITD-Pre), which contains three main modules: (1) the knowledge graph (KG) module constructs a multilayer individual travel KG from top to bottom, which accurately describes the travel individuals and their travel intentions. By analyzing the association relationship between nodes in the KG, the relationship between travel individuals can be naturally established. The learned similar travel regularity can solve the problem of sparse travel trajectories of some individuals. (2) The enhanced Transformer module extracts the dynamic and hierarchical features from the long-term sequential travel trajectory data. (3) The classifier module introduces the cross-entropy loss to constrain the uniqueness of the predicted subway travel station. The experimental results show that the proposed method obtains a higher destination prediction accuracy than the previous individual travel destination prediction methods.

Passengers’ Sensitivity and Adaptive Behaviors to Health Risks in the Subway Microenvironment: A Case Study in Nanjing, China

Passenger behavior in subways has recently become a matter of great concern, with more attention being paid to the health risks of the subway microenvironment (sub-ME). This paper aimed to provide guidance for subway passengers on better adapting to the health risks presented by the sub-ME. A questionnaire-based survey was conducted in Nanjing, China, and descriptive analysis and a one-way analysis of variance were performed to understand the sensitivity levels of subway passengers and analyze their adaptive behaviors, based on their sensitivity to sub-ME health risks. The results showed that passengers over 66 years old and those who are frequently sick are more sensitive to the presented health risks. Additionally, passengers traveling for longer and those traveling in rush hours are more sensitive to sub-ME health risks. We also found that individual characteristics, knowledge structure, and information communication all influence passengers’ adaptive behaviors. It was ascertained that those with a positive attitude and those who had previously suffered from environmentally influenced diseases, as well as those who studied an environment-related subject, tended to demonstrate more adaptive behaviors. Moreover, passengers who are very familiar with the subway information communication channels and the related information adapted better to the health risks of the sub-ME. Our findings are beneficial for improving passengers’ adaptability to the health risks presented by the sub-ME and for promoting the sustainable operation of subway systems.

An Analysis of the Normative (Non)Compliance and the Interactions among Passengers in the Buenos Aires Subway

The subway is a public transportation where the control and freedom of passengers must be regulated to create a peaceful and harmonious environment. Social transgression imposes an environment of discomfort and tension between passengers. This study aims to describe the regulations that Buenos Aires subway passengers know, to investigate the perception of (non)compliance with them, to analyze how the interaction among the passengers in the subway is perceived and how it is related to (non)compliance with the norms of coexistence and safety. The results indicated that the participants perceived a high degree of normative noncompliance. Generalized noncompliance accounts for the existence of informal rules that organize the behavior of passengers in contradiction with the guidelines of formal rules. This double standard causes problems of coexistence and safety in the subway. Normative transgressions negatively affect the interactions among passengers, bringing about a social climate of discomfort and tension.

Cluster-based spatiotemporal dual self-adaptive network for short-term subway passenger flow forecasting

Cluster-based spatiotemporal dual self-adaptive network for short-term subway passenger flow forecasting

Short-Term Subway Inbound Passenger Flow Prediction Based on AFC Data and PSO-LSTM Optimized Model

Making accurate predictions of subway passenger flow is conducive to optimizing operation plans. This study aims to analyze the regularity of subway passenger flow and combine the modeling skills of deep learning with transportation knowledge to predict the short-term subway passenger flow in the scenarios of workdays and holidays. The processed data were collected from two months of Automated Fare Collection (AFC) data from Xizhimen station of Beijing metro. The data were first cleaned by the established cleansing rules to delete malformed and abnormal logic data. The cleaned data were used to analyze the spatial characteristics in passenger flow. Second, a short-term subway passenger flow prediction model was built on the basis of long short-term memory (LSTM). Determining that the error will be relatively high in peak hours, we proposed gradual optimizations from data input by dividing one whole day into different time periods, and then used particle swarm optimization (PSO) to search for the optimal hyperparameters setting. Finally, inbound passenger flow of Beijing Xizhimen subway station in 2018 was selected for numerical experiments. Predictions of the LSTM-based model had higher accuracy than the traditional machine learning support vector regression (SVR) model, with mean absolute percentage error (MAPE) of 21.97% and 4.80% in the scenarios of workdays and holidays, respectively, which are both lower than those of the SVR model. The optimized PSO-LSTM model has been verified for its effectiveness and accurateness by the AFC data.

Machine learning approach for study on subway passenger flow

We investigate regional features nearby the subway station using the clustering method called the funFEM and propose a two-step procedure to predict a subway passenger transport flow by incorporating the geographical information from the cluster analysis to functional time series prediction. A massive smart card transaction dataset is used to analyze the daily number of passengers for each station in Seoul Metro. First, we cluster the stations into six categories with respect to their patterns of passenger transport. Then, we forecast the daily number of passengers with respect to each cluster. By comparing our predicted results with the actual number of passengers, we demonstrate the predicted number of passengers based on the clustering results is more accurate in contrast to the result without considering the regional properties. The result from our data-driven approach can be applied to improve the subway service plan and relieve infectious diseases as we can reduce the congestion by controlling train intervals based on the passenger flow. Furthermore, the prediction result can be utilized to plan a ‘smart city’ which seeks shorter commuting time, comfortable ridership, and environmental sustainability.

A Novel Trajectory Based Prediction Method for Urban Subway Design

In recent years, with the development of various types of public transportation, they are also more and more closely connected. Among them, subway transportation has become the first choice of major cities. However, the planning of subway stations is very difficult and there are many factors to consider. Besides, few methods for selecting optimal station locations take other public transport in to consideration. In order to study the relationship between different types of public transportation, the authors collected and analyzed the travel data of subway passengers and the passenger trajectory data of taxis. In this paper, a method based on LeaderRank and Gaussian Mixed Model (GMM) is proposed to conduct subway station locations selection. In this method, the author builds a subway-passenger traffic zone weighted network and a station location prediction model. First, we evaluate the nodes in the network, then use the GPS track data of taxis to predict the location of new stations in future subway construction, and analyze and discuss the land use characteristics in the prediction area. Taking the design of the Beijing subway line as an example, the suitability of this method is illustrated.

Use of Personal Protection Equipment by Moscow Subway Passengers under Conditions of COVID-19 Pandemic

The Subway is the most important means of transport in Moscow. The active flows of passengers in the metro can contribute to the spread of infectious diseases with airborne and contact mechanisms of transmission, including the coronavirus infection COVID-19. The aim of the study was to assess the level of adherence to the use of personal protective equipment (facial masks, gloves) by passengers of the Moscow Subway. Materials and methods. A prospective study based on a visual assessment of passengers in a rolling stock of the Moscow subway, with division into cohorts of those using masks and gloves (K1), using only masks (K2), using masks in violation of the rules of use (K3) and not using personal protection equipment (K4) was conducted. Observations were carried out during the 42nd and 43rd weeks of 2020 in various subway clusters with a division in time into morning, afternoon and evening hours. Additionally, passengers were ranked into groups by occupation during the trip. Statistical processing included the identification of frequencies, their 95 % confidence intervals. To compare the significance of differences in the prevalence of features in paired groups, the methods of 4-field tables (χ2 Pearson) were used. Results and discussion. 18053 observations were validated, covering 61.3 % of stations. Men accounted for 54.7 % (9867). During the observation period, the following shares were established: K1 – 5.2 % (95 % CI 4.9–5.5), K2 – 51.9 % (95 % CI 51.2–52.7), K3 – 26.9 (95 % CI 26.2–27.6), K4 – 16 % (95 % CI 15.4–16.6). The level of adherence to the proper use of PPE is significantly lower among men. Land lines have the lowest proportion of passengers using PPE. Among passengers using electronic devices and paper media, a higher proportion of those wearing masks with violation of the rules of use, compared to passengers without activities, was established. Among passengers using electronic devices, the proportion of those using gloves is the lowest. Within two weeks of follow-up, a significant increase in the frequency of PPE use was revealed.