Passenger Flow Organization Research Articles

Оптимізація пасажирських перевезень на основі організації однорідного приміського пасажиропотоку

The purpose article is improveefficiency of suburban passenger transportation based on organization of suburban passenger traffic. It was established that suburban passenger flow urban agglomerations consistsof following aggregated groups (segments): workers with variable work schedules, office workers, students, other passengers, while the total volume of passenger flow and the value individual components its segments are unstable and characterized by spatial and temporal unevenness.
 The task organization of passenger traffic is find a compromise between the needs passengers and possibilities of transport. The compromise is achieved due fact by obtaining formal optimum each time, with help of variable weighting factors, it is possible display priorities passenger flows from different stations and sections. The task optimizing functioning of passenger transport system ofurban agglomeration based on organization passenger flow is minimize total costs transportation and waiting. The optimization criterion is determined based on minimization costs mastering passenger flow in full, as well as waiting by passengers at departure station and delay at destination station.
 The article formulates problem of organizing suburban passenger flows in urban agglomerations reduce static reserves of suburban passenger complex at expense dynamic ones. The optimization apparatus based on dynamic transport problem was chosen and substantiated. The possibilities using method of dynamic coordination solve transport problems of urban agglomerations are analyzed and advantages are shown. The technology using optimization apparatus is proposed, which includes directed iterative process, which allows reduce the number of experiments for choosing among optimal options, most client-oriented compromise option. The method optimal organization of homogeneous suburban passenger flow based on method dynamic coordination has been developed. It was established use directed iterative process allows significantly reduce number of experiments in order achieve consensus betweeninterests passengers and efficiency of transport system.

Passenger flow simulation of Xiamafang metro station based on AnyLogic

In this paper, based on the functional location layout and passenger flow distribution characteristics of Xiamafang metro station, the passenger flow of Xiamafang metro station is simulated and analyzed with the help of the traffic behavior model theory of pedestrians by using the AnyLogic micro dynamic simulation software, the traffic behavior, simulation method and simulation effect are studied. Firstly, it introduces the actual situation of Xiamafang metro station, including the analysis of station layout and passenger flow organization streamline. Secondly, the pedestrian micro simulation model and implementation method are studied, including the social force model, the introduction of AnyLogic software and the implementation steps of pedestrian simulation. Finally, in the case of simplifying the layout of facilities in Xiamafang metro station, the simulation model of subway station is established by using AnyLogic software, the simulation effect is studied, the main problems of passenger flow in the station are analyzed, and the optimization measures are put forward for the bottleneck problems, which provides the basis for the actual operation and management decision-making.

The Dynamic Programming Model for Calculating the Maximum Number of Gathering Passengers in High-Speed Railway Station Based on Gaussian distribution

in this paper, based on the actual train operation plan and evacuation plan of the high-speed railway station, taking the train capacity and occupancy rate as the basic data, the Gaussian function is used to describe the passenger arrival law and the uniform distribution is used to describe the passenger evacuation law. Also, the calculation model of the number of gathering passengers at the high-speed railway station is established by MATLAB. Taking Guangzhou South Railway Station as an actual case, the maximum number of gathering passengers in the whole day and the change curve of the number of gathering passengers with time are obtained. The calculation results are consistent with the actual situation, which can provide a reference for the passenger flow organization and management of the high-speed railway station and the new construction and reconstruction of the high-speed railway station.

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.

Multi-point short-term prediction of station passenger flow based on temporal multi-graph convolutional network

Prediction of passenger flow distribution in urban rail transit stations can provide important data support for passenger flow organization and passenger travel guidance. However, complex station space structure and simulation-based passenger flow data bring challenges to accurate analysis and prediction of the passenger flow inside the station. This paper proposes a temporal graph attention convolutional neural network model (TGACN) to predict the passenger flow volume and density in key areas of the station. Firstly, considering the topological structure of key areas and the characteristics of passenger flow and flow trend in the station, a multi-graph generation method for continuous space in stations is designed, including geographic neighborhood graph and semantic neighborhood graph, to represent the static and dynamic correlation between nodes. Secondly, a new method of spatio-temporal feature fusion is proposed, which takes multi-graph as input to optimize the extraction and expression of spatial and temporal correlation. Finally, the TGACN is verified by passenger flow data set, which is constructed based on real-time video monitoring data of a transit station in Guangzhou. Experiments demonstrate that the TGACN can obtain the spatio-temporal correlation from passenger flow data, and the prediction results are better than the existing baseline models.

Формирование конкурентной среды на примере транспортного потенциала методом организации поточного следования пассажиров на основе принципа «сухие ноги»

Purpose: Formation of the system of transport potential and universalization of a railway and urban (municipal) transport system for a passenger flow according to “dry feet” principle. Methods: The main tools for the analysis and management of a passenger complex at implementation of ongoing activities are: cause-and-effect links, algorithmization, mapping, data analysis, etc. Results: Variant for organization and implementation of ergonomic and rational planning of space of railway and urban environment by the method of passenger flow organization based on the principle of “dry feet” is presented. Practical importance: using the obtained algorithm (sequence) as an example, to provide a client with an unhindered and comfortable pass along an entire route: from a route beginning, along a route, during transfers and a route end regardless of transport mode used.

Simulation analysis of epidemic prevention and regulation for metro passenger flow based on system dynamics

According to the spreading mechanism of corona virus disease 2019 (COVID-19), the passengers in the metro system have a certain risk of being infected because the metro is a large capacity public transport mode with high passenger density in the stations. Relevant operational agencies need to organize passenger flow and take measures effectively to reduce infection risk in stations. In this paper, we establish a system dynamics model to simulate the passenger flow organization and distribution in metro stations. Then, we build an improved calculation model for the traffic-infected susceptibility and use it to predict the probability of infection. Four passenger flow control schemes are evaluated and their impacts on susceptibility are quantitatively analyzed. The four passenger flow control schemes include limiting the inbound passenger flow, controlling the number of service facilities, extending the streamline length of the station hall, and increasing the frequency of metro trains. The simulation results show that limiting the passenger flow is more effective in reducing the susceptibility of the unpaid area, increasing the frequency of metro trains is more effective in reducing the susceptibility of the paid area, and extending the streamline length of the station hall is more effective in reducing the susceptibility of the platform. We find that system dynamics modeling is an effective means to formulate and assess passenger flow organization schemes and control measures. The simulation results can provide a reference for metro station operational departments to take scientific epidemic prevention measures.

Study on the connection mode of comprehensive hub based on urban traffic network

Since the 21st century, with the rapid development of various modes of transportation, the network integrated transportation hub planning is undergoing new changes. Comprehensive hub is not only an important carrier to connect multiple transportation modes, but also reflects the overall transportation efficiency and service level of different transportation modes. How to improve the passenger flow organization efficiency of urban comprehensive transportation hub is of great practical significance. Based on the proportion of traffic hub functional areas as the breakthrough point, relying on a comprehensive traffic hub in Binhai New Area of Tianjin, this paper aims to optimize the weight relationship among the functional areas of the comprehensive traffic hub, allocate the location and spatial sequence relationship of each functional area based on the weight relationship, and discuss the problem of improving the passenger flow organization efficiency of the comprehensive traffic hub and the corresponding measures. The research results show that intercity station buildings have high proportion and large scale in the comprehensive hub, and should be arranged in an important central position.

Research on Space-Time and Network Characteristics of Interprovincial Passenger Flow during the 2019 Spring Festival Transportation

To build the Spring Festival travel OD matrix of the Spring Festival of 2019 based on Baidu's inter-provincial migration data, China's inter-provincial passenger travel time and space features, the network passenger flow, and the characteristics of inter-provincial travel in different periods of Spring Festival travel in different provinces are analyzed by indicators, such as the volume of inter-provincial travel, inter-provincial migration, inter-provincial migration and net migration, the horizontal contrast of the characteristics of inter-provincial travel scale of 34 provincial administrative units across the country. The research conclusions are as follows. The inter-provincial travel of tourists has significant regular characteristics before and after the Spring Festival. The scale and time distribution characteristics of the rework flow before and after the Spring Festival can be used as an important basis for studying the social and economic activity levels based on the Spring Festival travel rush; 15 provinces with net inflow and 19 provinces with net outflow were identified. Among them, 10 provinces, such as Yunnan and Gansu, had average daily net inflows/outflows of less than 3000 people, which were relatively balanced. The 40-day Spring Festival travel period is divided into five periods according to the law of inter-provincial passenger flow. Different periods have different characteristics of passenger travel scale, providing a basis for the prediction of passenger flow and transportation capacity organization. Ten popular lines of inter-provincial travel before and after the festival were identified, indicating that the inter-provincial travel of Spring Festival passengers has an obvious spatial directivity, and the start and end of travel before and after the Spring Festival, the outflow city from the beginning or end of travel, and the inflow of the source city present a symmetric relationship. The analysis of Guangdong, Anhui, and other provinces with a large population mobility shows that they have the closest relationship with the travel of surrounding provinces, and the surrounding provinces with the top three inter-provincial trips account for more than 50% of the inter-provincial trips.

Simulation for Service Facilities and Passenger Flow Organization Optimization on Railway Terminal

This paper takes railway terminal as research object, considers the diversity of passengers, and uses Anylogic software based on the social force model to build a dynamic simulation model of inbound walking path for complex passenger flow. BeijingWest Railway Station is taken as an example, and equipment utilization balance degree (EUBD), passenger density (PD), average walking time (AWT), passenger queue length (PQL) are selected to evaluate, and optimization suggestions are put forward. Research shows that increasing the number of security inspection equipment, escalators can significantly improve the efficiency of passengers entering the terminal, and the average walking time at the entrance on the northwest and middle side of the second floor is reduced by 73.83%, 65.99% respectively.

Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data

Short-term prediction of passenger volume is a complex but critical task to urban rail companies, which desire prediction methods with high accuracy, time efficiency and good practicality. Good prediction results of the outbound passenger volume at urban rail stations are important to the organization of passenger flow, and helpful to the arrangement of shuttles, especially in large transit junctions. The application of automatic fare collection (AFC) devices in urban rail transit systems helps to collect large amounts of historical data of completed journeys, which can be used by metro operators to construct a database of the urban rail passenger volume. Based on deep learning techniques and big data, this paper develops an improved spatiotemporal long short-term memory model (Sp-LSTM) to forecast short-term outbound passenger volume at urban rail stations. The proposed model predicts the outbound passenger volume on the basis of the historical data of the spatial-temporal passenger volume, station origin–destination (OD) matrix and the operation data of the rail transit network. Finally, based on actual data of the Beijing Metro Airport Line, a case study is carried out to compare the proposed Sp-LSTM with other prediction methods, i.e., the general long short-term memory model (LSTM), the autoregressive integrated moving average model (ARIMA), and the non-linear autoregressive neural network model (NAR), and the results show that the proposed method outperforms the others.

Real-Time Passenger Flow Anomaly Detection Considering Typical Time Series Clustered Characteristics at Metro Stations

AbstractReal-time anomaly detection at metro stations is a very important task with considerable implications for massive passenger flow organization and train timetable rescheduling. State-of-the-...

基于客流分析的成都东站旅客流线组织优化

基于客流分析的成都东站旅客流线组织优化

Urban Rail Transit Passenger Flow Forecasting Method Based on the Coupling of Artificial Fish Swarm and Improved Particle Swarm Optimization Algorithms

Urban rail transit passenger flow forecasting is an important basis for station design, passenger flow organization, and train operation plan optimization. In this work, we combined the artificial fish swarm and improved particle swarm optimization (AFSA-PSO) algorithms. Taking the Window of the World station of the Shenzhen Metro Line 1 as an example, subway passenger flow prediction research was carried out. The AFSA-PSO algorithm successfully preserved the fast convergence and strong traceability of the original algorithm through particle self-adjustment and dynamic weights, and it effectively overcame its shortcomings, such as the tendency to fall into local optimum and lower convergence speed. In addition to accurately predicting normal passenger flow, the algorithm can also effectively identify and predict the large-scale tourist attractions passenger flow as it has strong applicability and robustness. Compared with single PSO or AFSA algorithms, the new algorithm has better prediction effects, such as faster convergence, lower average absolute percentage error, and a higher correlation coefficient with real values.

Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow.

Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.

Analysis of Pedestrian Walking Microscopic Characteristics in Urban Rail Transit Station Different Types Passageway

In order to study the microscopic characteristics of pedestrians in different types of traffic in urban rail transit stations, data on the field were collected and used to compare and analyze the basic parameters of pedestrian walking in various aspects, including different types of passageways in urban rail transit stations, one-way passageway and two-way passageway, passageway to and from the station and transfer passageway, as well as upper and lower slope passageway and horizontal passageway. The following conclusion is drawn: pedestrian walking speed in commuter stations is the highest, and coming next are pedestrians in transfer hub stations and residential stations, while pedestrians walk the slowest in commercial center stations and sightseeing stations; Pedestrian walking speed in one-way passageway is higher than that in two-way passageway, and the pedestrian conflict mainly affects the stride parameters; Pedestrian walking speed in transfer passageway is lower than that in passageway to and from the station; Inbound and outbound pedestrians differ in walking characters depending on their travel purposes and properties of rail stations; In a passageway with slope measured at 15%, every walking parameters of pedestrians in uphill passageway is lower than that of pedestrians in horizontal passageway, the walking speed, specifically, is reduced by 13.74%; Compared to pedestrians in horizontal passageway, pedestrians in downhill passageway has higher walking frequency and smaller stride. The conclusion can provide the basic parameters for micro-traffic simulation, as well as reference data to implement the urban rail transit passenger station design, passenger flow organization, facility layout optimization and facilities evaluation. The theory of pedestrian traffic is also enriched.

Calculation Method for Load Capacity of Urban Rail Transit Station considering Cascading Failure

The load capacity of urban rail transit station is of great significance to provide reference in station design and operation management. However, it is difficult to carry out quantitative calculation quickly and accurately due to the complex interaction among passenger behaviors, facility layout, and the limit capacity of single facility. In this paper, the association network of facilities is set up based on the analysis of passenger service chain in station. Then the concept of cascading failure is introduced to the dynamic calculation model of load capacity, which is established on the user-equilibrium allocation model. The solution algorithm is optimized with node attack strategy of complex network to effectively reduce the computational complexity. Finally, a case study of Lujiabang Road Station in Shanghai is carried out and compared with the simulation results of StaPass, verifying the feasibility of this approach. The proposed method can not only search for the bottleneck of capacity, but also help to trace the loading variation of facilities network in different scenarios, providing theoretical supports on passenger flow organization.

A high-fidelity three-dimensional simulation method for evaluating passenger flow organization and facility layout at metro stations

This research proposes a high-fidelity simulation method that integrates the strengths of microscopic pedestrian modeling and three-dimensional (3D) virtual reality (VR) for the evaluation of passenger flow organization and facility layout at metro stations. Passenger flow movements, with detailed spatial–temporal information of each agent, are modeled using agent-based micro-simulation. Simulation results are then imported into a 3D VR environment for the assessment of passenger flow organization and its interactions with facility layout. Focusing on the technical implementation of the method, we discuss in detail (1) finely resolved pedestrian simulation using AnyLogic; (2) bookkeeping of spatial positions of agents at the specific temporal intervals in XML format; and (3) interactive photo-realistic 3D visualization of pedestrian movements using UC-win/Road through its micro-simulation player plug-in, OpenMicroSim. Successfully applied to case studies of three major metro stations in Shenzhen China, this methodology is demonstrated to be capable of evaluating passenger flow organization through visual and quantitative analyses for improving facility layout design.

Holiday Passenger Flow Forecasting Based on the Modified Least-Square Support Vector Machine for the Metro System

AbstractHoliday passenger flow forecasting is essential to transportation plan-making and passenger flow organization in metro systems during holidays. Usually, daily passenger flow characteristics...

Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study

This paper proposes a problem of passenger flow organization in subway stations under uncertain demand. The existing concepts of station service capacity are extended and further classified into three in different demand scenarios. Mathematical models are put forward to measure the three capacities and a unified simulation-based algorithm is developed to solve them. To increase computing speed, data envelopment analysis (DEA) and genetic algorithms (GA) are embedded in this algorithm. A case study will demonstrate the performance of the proposed algorithm and give a detailed procedure of passenger flow control based on station service capacity in various demand scenarios.