Rail Transit Passenger Flow Research Articles

Forecasting metro rail transit passenger flow with multiple-attention deep neural networks and surrounding vehicle detection devices.

In the rapid development of public transportation led, the traffic flow prediction has become one of the most crucial issues, especially estimating the number of passengers using the Mass Rapid Transit (MRT) system. In general, predicting the passenger flow of traffic is a time-series problem that requires external information to improve accuracy. Because many MRT passengers take cars or buses to MRT stations, this study used external information from vehicle detection (VD) devices to improve the prediction of passenger flow. This study proposed a deep learning architecture, called a multiple-attention deep neural network (MADNN) model, based on historical MRT passenger flow and the flow from surrounding VD devices that estimates the weights of the vehicle detection devices. The model consists of (1) an MRT attention layer (MRT-AL) that generate hidden features for MRT stations, (2) a surrounding VD (SVD) attention layer (SVD-AL) that generate hidden features for SVD devices, and (3) an MRT-SVD attention layer (MRT-SVD-AL) that generate attention weights for each VD device in an MRT station. The results of the investigation indicated that the MADNN model outperformed the models without multiple-attention mechanisms in predicting the passenger flow of MRT traffic.

State-dependent Multi-agent Discrete Event Simulation for Urban Rail Transit Passenger Flow

State-dependent Multi-agent Discrete Event Simulation for Urban Rail Transit Passenger Flow

Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis

Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis

Research of Passenger Flow Forecast of Urban Rail Transit Based on Data Mining

With the development of China's economy and society, urban construction is constantly improving, urban rail transit is becoming more mature, and people's demand for travel quality is getting higher and higher. However, the imperfect operation and management leads to the contradiction between supply and demand of urban rail transit. Passenger flow data of rail transit is the basis of operation scheduling, and accurate prediction can effectively improve the utilization rate of operating energy. In this paper, through data mining of passenger flow data, the law of passenger flow in time dimension is analyzed, and three different forecasting models are established for rail transit passenger flow data. Finally, the forecasting effects of each model are compared. The characteristics of passenger flow are analyzed in the time dimension, which shows the different changing rules of passenger flow on working days and rest days. In the discussion of the three forecasting methods, firstly, the time series forecasting method is realized by SPSS software, and the final model parameters are determined by unit root test, autocorrelation analysis, partial autocorrelation analysis and Bayesian information criterion. After that, the regression prediction model of support vector machine and BP neural network model are established by MATLAB. The former maps nonlinear passenger flow data into high-dimensional space to find linear relationship for prediction, while the latter realizes passenger flow prediction by establishing neural network model. Finally, by comparing the three prediction models, the results show that the average absolute error of BP neural network prediction method is 13%, which is 44% and 10% lower than that of time series method and support vector machine method, respectively, with high accuracy.

Short‐term passenger flow prediction for rail transit based on improved particle swarm optimization algorithm

AbstractThe subjectivity of selecting training parameters is an important factor affecting the accuracy of short‐term passenger flow prediction of rail transit by long short‐term memory (LSTM) neural network. In order to improve the prediction accuracy, an improved particle swarm optimization (IPSO) algorithm is proposed to optimize the LSTM. The size of the learning factor of the particle swarm optimization (PSO) algorithm is controlled by dynamic adjustment method to improve the global optimization and convergence ability of the algorithm. The number of hidden layer nodes, learning rate and iteration times of the LSTM are optimized by IPSO. The passenger flow data of Dongjiekou station of Fuzhou Metro Line 1 are selected for verification, and the proposed model is compared with the traditional prediction model. The results show that the LSTM optimized by the improved particle swarm optimization algorithm can effectively predict the short‐term passenger flow of rail transit. Compared with the PSO‐LSTM model, the root mean square error predicted by the IPSO‐LSTM model decreases by 10.87% in the peak period and by 26% in the off‐peak period. The results can provide theoretical and technical support for the optimization of rail transit operation scheme.

Structure Optimization and Governance of Multilevel Rail Transit Integration under the Background of a Metropolitan Area Based on the Industrial Internet of Things Security Data Fusion Method

In recent years, the urban rail transit network architecture has gradually grown, the contradiction between rail transit passenger flow and transport load has been deepening, and its carrying capacity has also been tested. Passenger flow risk has become the most important source of risk in rail transit. The key to restricting rail transit service quality is how to effectively monitor and manage rail transit passenger flow and provide accurate and convenient early warning to staff. In order to effectively manage the complex passenger flow scene, the premise is to distinguish the real-time state of its moving target. The time series data feature extraction and LSTM data fusion were used to analyze the traffic data sequence in the multilevel rail transit network model. The multilevel rail transit integration of the Internet of things is modeled by the method of data fusion. It can be seen from the experimental data that in the data fusion mode, the network comprehensive evaluation prediction value fitting effect can quickly converge, and the error rate is less than 4%. By comparing the mean square error (MSE) and mean absolute error (MAE) data of the traditional method and the experimental method used in this paper through two different datasets, it was understood that the MAE under the data fusion method was reduced by 8 compared with the traditional method, and the MSE was decreased by 33, indicating that this method can bring better simulation effect to the model. The improvement of the synergistic and complementary functional network and the acceleration of the efficient and convenient flow of elements are the inevitable results of the integration of multilayer rail transit in the metropolitan area.

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Based on spatial data dynamic analysis technology, the characteristics of urban rail transit passenger flow are analyzed. This study analyzes the passenger flow characteristics of urban rail transit from the perspective of operation and planning by using the dynamic analysis technology of spatial data, combined with social and economic development. Based on the analysis of research data from the study of passenger traffic and passenger traffic data measurement, the characteristics of passenger traffic from urban railways are derived from the time distribution, spatial distribution, and transfer of passengers waiting on the platform. The final length of line 3 is 39.7 m, the passenger volume is 102, with 11 people/day, the passenger flow density is 1.8/2.3, and the high section in peak hours is 2.4/3.2. By considering the various influencing factors, efforts should be made to develop urban rail transit projects to promote the growth of social and economic benefits.

Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM

Predicting rail transit passenger flow is crucial for modifying the metro schedule. To increase prediction accuracy, a model is proposed that combines long short-term memory (LSTM) with single spectrum analysis (SSA). Firstly, a stepwise decomposition sampling (SDS) strategy based on SSA progressive decomposition is proposed as a solution to the data leaking issue in traditional sequence decomposition. Then, based on this strategy, the passenger flow time series with complex features is decomposed into a relatively single trend and fluctuation component. Finally, the LSTM network is employed to perform short-term predictions on each component separately. The predicted value of each component is accumulated to obtain the original passenger flow’ predicted result. The example shows that, compared with the single LSTM and other hybrid models, the proposed method offers a greater overall prediction accuracy in the experimental days, and the method has specific applicability.

Design and Study of Urban Rail Transit Security System Based on Face Recognition Technology

Due to the relatively closed environment, dense personnel, large passenger flow, long line and wide coverage of urban rail transit, it is difficult to prevent terrorism. Identity recognition is a core element of security. The design and study of urban rail transit security system based on face recognition technology is proposed in this paper. Through the study on the face recognition algorithm of intelligent security system in urban rail transit, the related introduction of face recognition technology, and the analysis on the main mode of face recognition, carry out practical application design ideas. The results show that if FAR is set to a very low range, such as 0.1% or even 0.01%, meanwhile FRR can reach a very low level, such as less than 1%, such a system has practical value, otherwise it may face a large number of passenger affairs and complaints to be handled once applied. When FAR is set to 0.1% and N is 1.6 million, FRR can reach 2.1%. However, according to the test, when the picture quality deteriorates, such as when using a Webcam (image captured by a webcam), the FRR will increase by 2 to 3 times. In fact, if Webcam is used for recognition in Mugshot, the lowest FRR of the three top algorithms is only 5.21%.

Analysis of the Low-Carbon, Environmental-Friendly, Energy-Saving, and Emission-Reduction Evaluation Model of Urban Rail Transit Based on the Spatiotemporal Distribution of Passenger Flow

The development of the urban economy and the effect of linkage radiation are inseparable from the urban transportation system’s efficient operation. In the context of the new era, environmental pollution caused by economic development has gradually become an invisible killer that endangers human health and the atmospheric environment. It is a pillar industry of economic development, a key part of urban infrastructure construction, and a necessary guarantee for urban residents to travel and live, and it is important to develop a low-carbon, environmental-friendly, energy-saving, and emission-reduction potential for urban transportation systems. On the basis of a large number of literature research, this paper attempts to establish the role of an urban rail transit system in energy conservation and emission reduction in three aspects: residents’ travel behavior, ground transportation operation, and low carbon, energy conservation, and reduced emission under the influence of the urban rail transit system. Based on the temporal and spatial distribution characteristics of urban rail transit passenger flow, a relatively complete energy-saving and emission-reduction evaluation model is established. Through case analysis, it is verified that the model can effectively evaluate the effect of energy saving and emission reduction under different rail transit settings and its spatial and temporal distribution characteristics, and provides ideas and technical guidance for multidimensional quantitative analysis of urban rail transit carbon environmental protection, energy conservation, and emission reduction.

Machine learning based method for forecasting short-term passenger flow in urban rail stations

Urban rail transit features much strength, such as large capacity, safety and environment-friendliness, and it becomes a preferred choice for most passengers. It also plays a prominent part in solving urban traffic problems. In order to improve the operation efficiency of the urban rail transit system and achieve the goal of smart operations, this paper applies machine learning algorithms and completes the feature engineering of urban rail transit passenger flow data in terms of time, space and external factors. Based on passenger flow characteristics as collected, we build the short-term passenger flow forecast models, which include light gradient boosting machine (LightGBM) model, long short-term memory (LSTM) model, and LightGBM-LSTM fusion model. Besides, we construct the autoregressive integrated moving average (ARIMA) model and extreme gradient boosting (XGBoost) model for experiment comparison. Finally, we conduct the passenger flow forecasting experiments on the Hangzhou subway dataset based on the five prediction-models mentioned above. Then, five types of subway stations are selected (residential type, occupation type, residential-occupation type, business type, and transportation hubs type) and three accuracy evaluation indicators are chosen (mean absolute error, root mean square error and mean absolute percentage error) to evaluate the prediction accuracy of the five prediction models. The experimental results show that, the multi-feature machine learning model can effectively forecast urban rail transit short-term passenger flow which is difficult for the traditional time series model. However, the single model has poor adaptability to different types of subway stations. Compared with a single model, LightGBM-LSTM model equipped with merits of multiple models, fulfills a better function in forecasting and a better adaptability to different types of urban rail transit stations.

Measuring the Relationship between Influence Factor and Urban Rail Transit Passenger Flow: Correlation or Causality?

Measuring the Relationship between Influence Factor and Urban Rail Transit Passenger Flow: Correlation or Causality?

Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model

It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and the long short-term memory network (LSTM) based on machine learning is proposed. The model couples the external factors such as date attributes, weather conditions, and air quality, to improve the overall prediction performance and solve the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. Using the swiping data and related weather information of some stations of Chongqing Rail Transit Line 3, the TCN-LSTM model is verified by an example, and the prediction results of the single LSTM model are given for comparison. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times. Compared with the single LSTM model, the TCN-LSTM model has better prediction accuracy and data generalization ability.

Modelling cascade dynamics of passenger flow congestion in urban rail transit network induced by train delay

A research into congestion propagation mechanism of urban rail transit passenger flow in train delay scenario is of great significance to the formulation of coordinated limitation measures of passenger flow among the operation lines in urban rail transit networks. This paper presents a methodology for modelling cascade dynamics of passenger flow congestion. In this method, firstly, a computing method of network passenger flow states based on load entropy is proposed to describe the overall load conditions of passenger flow in the network. Secondly, a rail transit network is developed and the computation formulas of load and capacity of nodes have been established combining with a comprehensive consideration of nodes’ topological passenger flow attributes. Thirdly, Trip Betweenness Centrality of nodes is proposed and calculated. Considering the function of rail transit line capacity and load distribution strategy, the passenger flow congestion in train delay scenario is established to calculate cascade dynamics of congestion and the state transition of urban rail transit network. A real case study on Beijing Metro Line 10 is used to demonstrate the proposed methodology. The results show that the load values of nodes and the distances from the initial failure nodes can be determined for the spreading scope of passenger flow congestion in disrupted metro line and adjacent metro lines.

An Accurate Od Identification Algorithm for Rail Transit Passenger Flow

An Accurate Od Identification Algorithm for Rail Transit Passenger Flow

Analyzing Influencing Factors of Transfer Passenger Flow of Urban Rail Transit: A New Approach Based on Nested Logit Model Considering Transfer Choices.

With the continuous improvement of the operation line network of urban rail transit, analyzing influencing factors of transfer passenger flow of urban rail transit is critical to improve the transfer demand analysis of urban rail transit. Using data collected from questionnaires, transfer passenger flow surveys and smart cards, this study proposes an approach base on nested logit passenger flow assignment model considering transfer choice behaviours of passengers. The transfer passenger flow at seven transfer stations in Nanjing is obtained. Subsequently, this study investigates the potential influencing factors of transfer passenger flow, including the node degree, geographic location (located in the city center, urban fringe, suburbs or suburban fringe), economic location (distance from the city center) and transportation locations (if it is close to a transportation hub or in combination with the hub) of rail transit transfer stations. The results indicate that a positive correlation between the transfer passenger flow and the node degrees of transfer stations. However, the relationship between transfer passenger flow and the economic, geographic, and transportation locations of transfer stations is not clear. The finding have reference value for the network design of rail transit transfer stations and transfer facilities, and provide reference for the analysis of passenger flow under network operation.

A novel prediction model for the inbound passenger flow of urban rail transit

High-precision short-term inbound passenger flow prediction is of great significance to the daily crowd management and line rescheduling in urban rail systems. Although current models have been applied to prediction, most methods need optimization to meet refined passenger flow management demand. In order to better predict the passenger flow, a novel Wave-LSTM model, based on long short-term memory network (LSTM) and wavelet, is introduced in this paper. In an empirical study with practical passenger flow data of Dongzhimen Station in the Beijing Subway system, the hybrid model exhibited more effective performance in terms of prediction accuracy than the existing algorithms, e.g., autoregressive integrated moving average (ARIMA), nonlinear regression (NAR), and traditional LSTM model. The study illustrates that our newly adopted model is a promising approach for predicting high-precision short-term inbound passenger flow.

Research on digital flow control model of urban rail transit under the situation of epidemic prevention and control

Purpose Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station. Design/methodology/approach This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train. Findings A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow. Originality/value This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.

Short-term passenger flow forecast for urban rail transit based on multi-source data

Short-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction.

CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions

Emergencies have a significant impact on the passenger flow of urban rail transit. It is of great practical significance to accurately predict the urban rail transit passenger flow and carry out research on its temporal and spatial distributions under emergency conditions. Urban rail transit operating units currently use video surveillance information mainly to process emergencies and rarely use computer vision technology to analyze passenger flow information collected. Accordingly, this paper proposes a passenger flow-based temporal and spatial distribution model for urban rail transit emergencies based on the CPT. First, this paper clarifies the categories and classification of urban rail transit emergencies, analyzes the factors affecting passenger route selection, and establishes a generalized travel cost model for passengers under emergencies. Second, this paper establishes a passenger route choice behavior model for urban rail transit based on the cumulative prospect theory. Finally, taking Beijing as an example, this paper analyzes passenger travel behavior under emergencies based on multiple logistic regression models and analyzes the impact of emergencies on rail transit travel behavior. The research results show that the cumulative prospect theory can better describe the route choice behavior of rail transit passengers under emergencies than the existing models, and this model is of great significance for handling urban rail transit emergencies. The model proposed in this paper can provide a theoretical basis for the government and relevant departments to formulate traffic management measures.