Passenger Flow Research Articles

Vulnerability simulation and evaluation of urban metro operation system: A hybrid structural equation model with system dynamics approach

To effectively identify the influence factors of urban metro operation system (UMOS) vulnerability and research the dynamic evolution process of system vulnerability, ensuring the long-term safety of UMOS, a hybrid method integrating structural equation model (SEM) and system dynamic (SD) is proposed to evaluate the UMOS vulnerability. The vulnerability evaluation indexes system of UMOS is established by questionnaire design, which includes four dimensions of personnel, equipment, management and environment. The effectiveness of the method is verified by taking Wuhan metro system in China as an example. The results indicate that (1) The influence of personnel, equipment, management and environment factors on UMOS is 30.2 %, 27.6 %, 21.6 % and 20.6 %, respectively. (2) The passenger flow level and emergency rescue system level have the greatest influence on the vulnerability of UMOS via sensitivity analysis. (3) Extreme weather conditions have the greatest impact on the environment sub-system vulnerability, which in turn affects the UMOS. Hence, the proposed method herein can simulate the long-term vulnerability of the entire UMOS and formulate corresponding improvement measures, thereby achieving sustainable transportation development.

Time Granularity Setting Principle for Short-Term Passenger Flow Prediction in Urban Rail Transit

Time Granularity Setting Principle for Short-Term Passenger Flow Prediction in Urban Rail Transit

A COMPARATIVE STUDY OF HEADWAY-BASED AND TRANSPORT SYSTEM-BASED ASSIGNMENTS OF PUBLIC TRANSPORT IN VISUM: THE CITY OF KRYVYI RIH CASE

Many researchers have explored public transport assignment methodologies employing transport modelling software. Nevertheless, there remains a gap in evaluating real-world public transit networks utilizing diverse assignment procedures within Visum software. This paper introduces a thorough comparison of algorithms involved in public transport assignment processes, using the transport model of the city of Kryvyi Rih in Ukraine. The three scenarios of the model were developed depending on the public transport assignment procedure: headway-based, transport system-based utilized to all links, turns, and major turns in the network graph, and transport system-based applied only to the links, turns, and major turns traversed by the active public transport lines. The model of the network comprises 13 transport systems, 7 transport modes, 27598 links, 10097 nodes, 83270 turns, 238 zones, 1748 connections for private transport, 3013 connections for public transport, 534 stops, 1165 stop areas, 1190 stop points, 130 lines and 218 line routes. The transport demand model encompassed 14 demand segments. Compared to the outputs of the model calculation using the headway-based procedure, in the scenarios with transport system-based assignment, passenger flows on rail tram lines significantly decreased. Also, the results of scenarios with transport system-based assignment showed that the passengers extremely use parallel streets for travel from an origin to a destination. The modelling outputs for an actual urban network illustrate that the choice of the shortest route in the transport system-based assignment is closely linked to the main urban roads. This circumstance could potentially result in a rise in the number of transfers needed to sustain accessibility to districts residing far from the central highway.

Разработка комплексного показателя оценки состояния городского электротранспорта для органов исполнительной власти Российской Федерации

This article analyzes 115 cities on the territory of the Russian Federation in which one or more urban electric transport systems operate. It is determined that in the modern industry there is no single comprehensive indicator of the state of urban electric transport, which would allow an assessment of the state of systems not only in relation to their own indicators of previous years, but also in relation to other, similar systems. A comprehensive indicator is proposed that will allow a comprehensive assessment of the state of urban electric transport and interpret it in numerical terms for its application by state and regional authorities, allowing to quickly draw conclusions and make a decision on further adjustment of the functioning of the electric transport system, characterized by a relatively simple formula for calculation and does not require specific competencies for those conducting the analysis. The key indicators of urban electric transport are systematized, including those related to the operated energy infrastructure, linear infrastructure, park facilities, including technical buildings and structures for system maintenance, indicators related to rolling stock and its key technical characteristics, as well as the availability of urban electric transport for residents of the considered locality and passenger flows within this framework systems. The boundaries of the values of the final indicator have been determined, within which the state of urban electric transport will be assessed by state and regional authorities as satisfactory or unsatisfactory and requiring decisions on revising approaches to the development of the system within the framework of strategic transport planning documents.

Collaborative train timetabling and passenger flow control in oversaturated metro lines considering state-dependence

Congestion downgrades the moving speed of passengers in trains and platforms. This in turn affects congestion. That is, the moving speed of passengers interacts with congestion. It is state-dependent and obeys the fundamental diagram. This interaction makes the system unstable and can result in substantial passenger delays and heightened security risks in oversaturated metro lines. This study aims to rectify this by developing a more realistic collaborative optimization approach. Firstly, we formulate a deterministic fluid queuing network model to accurately capture the dynamic propagation of congestion in oversaturated metro lines. This model incorporates the fundamental diagram, thereby effectively considering the interaction between passenger movement speeds and congestion. A recursive algorithm is devised to solve the fluid queuing network model. The complexity of the algorithm is independent of the capacities of the stations and trains. This provides the possibility of analyzing large-scale metro networks. Based on the queuing network model, a mixed-integer nonlinear programming model aimed at minimizing the total passenger waiting time is built. To solve the model effectively, we employ an alternating direction method of multipliers framework to decompose the model into nonlinear programming and integer nonlinear programming problems, which are respectively solved by improved simultaneous perturbation stochastic approximation and adaptive large neighborhood search algorithms. Finally, a small-scale case and a real-world case with data from Chengdu metro lines are implemented to demonstrate the performance and effectiveness of the proposed approach. The experimental results provide some interesting insights. (1) Without consideration of state-dependence, the queuing model underestimates the passenger waiting time and overestimates the service levels. (2) As demand increases, excluding extreme oversaturation scenarios, the optimized results of the state-independent model progressively deteriorate compared to our state-dependent model, with the largest percentage decline being 50.23%. This is especially noticeable when the train capacity is limited. (3) The contribution ratio of passenger flow control in mitigating congestion gradually surpasses that of train timetabling with increasing demand. In the small-scale case, the former contribution ratio to optimization rises from negligible to 98.19%.

РАЗРАБОТКА КРИТЕРИЕВ, ОПРЕДЕЛЯЮЩИХ БЕЗОПАСНОСТЬ И КАЧЕСТВО ТРАНСПОРТНОГО ОБСЛУЖИВАНИЯ НАСЕЛЕНИЯ ГОРОДА

The quality system should provide for quality management at all stages of the life cycle of passenger transportation services, starting from the study of passenger flows and forecasting demand for transportation (transportation marketing) and ending with the analysis of services provided according to operational accounting data. In this article, based on the results of studies of various systems, the main criteria for building a quality management system for passenger transport enterprises are proposed, as well as their organizational distribution, the use of which will make it possible to form effective requirements for the quality of passenger transportation.

Impact of high-speed railway construction on freight capacity on sections of existing railways- A case on the Yangtze River Delta

The construction of high-speed railway (HSR) can effectively release the freight capacity of existing railways. By analyzing the influencing factors of passengers’ travel choices, the Utility Function and Logit model are adopted to calculate the utility and passenger flow split rate, and get the passenger flow of the existing railways after the diversion of the newly-built HSR. The passenger flow allocation model is established based on User Equilibrium, and improved Frank-Wolfe (FW) algorithm is applied to solve it, and obtain the passenger capacity of the sections on existing railways. Based on the deduction coefficient method, the passenger capacity is deducted from total capacity to get the freight capacity of each section on existing railways after release. Finally, take the railway channel in the Yangtze River Delta as a case, the results show that after the construction of the HSR, the freight capacity of sections on existing railways in the Yangtze River Delta is released to varying degrees. The freight capacity of Hefei-Wuhu section will be released the most, which is 19.5 pairs of trains, while that of Shanghai-Jiaxing section is the least, which is only 3.5 pairs, following by Wenzhou-Jinhua and Hangzhou-Ningbo sections, which are 4 pairs. Therefore, the construction of HSR should be accompanied by focusing on sections with less released freight capacity to enhance the overall capacity of the regional railway network.

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 ).

A dynamic evaluation of an underground transportation system using image processing and centrality index computation

ABSTRACT The study of transportation networks covers a wide range of topics related to managing services and maintaining structures. In order to represent the topological structure of such a network, we associated a graph to a network. Then some different properties or attributes of the network can be managed as weights to be applied to the graph itself. In addition, different centrality indices can be computed in order to identify nodes or edges that are more important or more exposed to capacity changes (such as, for example, disruptive events). The purpose of this research is to provide a methodology for dynamically evaluating the significance of stations in a transportation network. This is accomplished through two distinct phases of activity. Firstly, we propose a dynamic analysis of the underground transportation system of the city of Milan, Italy. This transportation system is the mobility backbone of the city and counts four lines with 110 links and 107 stations, including 21 junctions (the transfer stations that connect the lines). Two data sets about passenger flows (both entering and leaving stations) are used to calculate the flows on links with a resolution of 1 min. The data sets refer to a week in 2018, without the changes in demand due to the pandemic scenario. Data are processed through an ad hoc written assignment procedure. Secondly, a centrality index is calculated by using passenger flows (entering, exiting, and on-segments) as weights of the underground graph. After maps reporting the outcomes of those calculations are drawn, an image comparison is carried out by using image processing tools and different aggregation intervals, in order to investigate how the importance or exposure of each station changes over time. The findings demonstrate that over time, indices vary by station, with junction stations naturally having the highest values. By increasing the observation interval from 1 min up to 30 min, index changes become progressively smoother, like an application of a low pass filter, suggesting that for certain applications (e.g. those concerning security), aggregating data could lead to misleading conclusions.

Two-step Passenger-to-Train and Route Assignment Model for Urban Rail Transit

Accurate passenger flow assignment results are crucial for the operational organization of urban rail transit. However, because of barrier-free transfer, the transfer information of passengers is not recorded. The paper proposes the two-step passenger-to-train and route assignment (TPTRA) model to address this issue. Each passenger is assigned to the trains and one route considering the complex transfer situation using TPTRA compared with the existing passenger-to-train model. Constraints on passengers, trains, and routes are constructed based on a large amount of real automatic fare collection data, train schedules, and metro topology networks to improve accuracy and transferability over traditional survey-based methods. The generalized logistic is first used to evaluate the egress time, access time, and transfer time distributions. Finally, TPTRA is developed based on the constraints and distributions. The model estimates the probability of passengers choosing the feasible routes and boarding the first train. It also calculates transfer passenger flow values and the probability of passengers being left behind. The analysis demonstrates a notable enhancement in the accuracy of the egress time distribution fitting using the generalized logistic distribution, with an improvement of 84.41% compared with the lognormal distribution and 15.78% compared with the normal distribution. Furthermore, the model’s effectiveness is validated through sectional passenger flow comparisons, yielding R-squared values between 0.847 and 0.902, indicating the effectiveness and reliability of the TPTRA model.

Multi-Step Passenger Flow Prediction for Urban Metro System Based on Spatial-Temporal Graph Neural Network

Efficient operation of urban metro systems depends on accurate passenger flow predictions, a task complicated by intricate spatiotemporal correlations. This paper introduces a novel spatiotemporal graph neural network (STGNN) designed explicitly for predicting multistep passenger flow within metro stations. In the spatial dimension, previous research primarily focuses on local spatial dependencies, struggling to capture implicit global information. We propose a spatial modeling module that leverages a dynamic global attention network (DGAN) to capture dynamic global information from all-pair interactions, intricately fusing prior knowledge from the input graph with a graph convolutional network. In the temporal dimension, we design a temporal modeling module tailored to navigate the challenges of both long-term and recent-term temporal passenger flow patterns. This module consists of series decomposition blocks and locality-aware sparse attention (LSA) blocks to incorporate multiple local contexts and reduce computational complexities in long sequence modeling. Experiments conducted on both simulated and real-world datasets validate the exceptional predictive performance of our proposed model.

Effects of Transport Infrastructure Development on the Socioeconomic Wellbeing of Baro Residents: A Study of Baro Inland Port in Niger State

The development of Baro inland port is heavily reliant on the availability of robust transport infrastructure and accessibility, which is crucial for enhancing the socioeconomic wellbeing of Baro residents. This study investigates the effects of transport infrastructure development on the socioeconomic wellbeing of Baro residents. Primary data were collected through a questionnaire survey, with 394 questionnaires administered randomly to Baro residents. A total of 350 completed questionnaires were returned and analyzed using descriptive statistics (simple percentage) and chi-squared analysis. The findings reveal that transport infrastructure development has a significant positive impact on socioeconomic wellbeing, leading to improved access to education, healthcare, and employment opportunities. To build on this, prioritization of access road and rail network construction and rehabilitation is recommended to facilitate efficient passenger and goods movement. Furthermore, the involvement of Public-Private Partnerships (PPP) is suggested by this study to address the challenges facing Baro inland port and enhance the flow of passengers and goods.

The temporal distribution of ridership in metro stations from land-use perspective.

A reasonable land use development around subway stations can balance the utilization rates of the subway system during peak and off-peak hours, thereby enhancing its service levels and operational efficiency. Analyzing the temporal distribution patterns of passenger flow and their influencing factors is crucial for determining the optimum ratio of each land use type surrounding metro stations. Thus, this paper employs principal component analysis (PCA) at first to investigate the temporal distribution of metro ridership, and identify their main patterns and factor loadings. Then, using geographically weighted regression, the study examines the spatial dependencies between the main component proportions and influencing factors, focusing on Xi'an subway stations. The results indicate that the temporal distribution of passenger flow can be decomposed into three principal components: the first representing commuting characteristics, and the second and third representing regulating functions. The overall distribution is a composite of these components in varying proportions. Residential and educational land uses primarily drive morning and evening peak flows, with residential land use in the city center and peripheral areas having a more pronounced effect compared to transitional areas. Conversely, commercial & office, healthcare, and recreational & park land mitigate peak flows and increase off-peak flows. External hub enhances passenger flow throughout the day, while industrial land use has negligible impact.

Research on Intercity Railway Subsidy Mechanism Optimization from the Perspective of a Government–Company Game Model: A Case Study of Henan Intercity Railway

Intercity railway is an important part of public transportation, and the priority development of public transportation cannot be achieved without the support of government policies. This paper aims to find a more reasonable subsidy model for the governments and intercity railway companies. The paper analyzes the mechanism of intercity railway subsidy and uses the evolutionary game method to balance interests among governments, railway companies and social capitals. The main conclusions are as follows: (1) The introduction of performance subsidy into the traditional loss subsidy strategy can alleviate the conflict of interests between government and companies, and achieve a win-win conclusion: an annual revenue of 50 million RMB for the railway and 4 million RMB for the social capitals could be generated by the new subsidy model. (2) According to different intercity railways, reference performance standards are different, and operating mileage, pairs of trains and passenger flow are some of the factors that can be considered. The innovation of this paper is the introduction of a new dynamic subsidy model that combines performance and loss subsidies to intercity railways. For sustainable transportation development, it is significant for the government to develop a new reasonable intercity railway subsidy strategy.

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.

Collaborative rescheduling of train timetables to relieve passenger congestions in an urban rail transit network: A rolling horizon approach

At some urban rail transit (URT) stations, large activities, emergencies, or holidays usually lead to a significant surge in passenger flow demand quickly, known as Large Passenger Flow (LPF) events. The passenger congestion will spread quickly via transfer stations and affect other stations and lines in the URT network. This study develops a timetable rescheduling and coordinating method for the URT network under LPF events. Firstly, a collaborative adjustment model of train timetables with an implement backup vehicle strategy is formulated to simultaneously consider rescheduling and coordinating problems, to reduce the congestion influence for an URT network. Then, a rolling horizon approach is developed to divide the whole adjustment problem into several decision-making stages to ensure solution efficiency. In each decision-making stage, the influence of LPF propagation within the URT network is firstly evaluated. Based on the congestion evaluation results, the proposed method determines whether it’s necessary to adjust timetables of the LPF Line or other lines. The proposed method is applied to the Xi’an Metro network in China. The results indicate that the proposed method can effectively evaluate and adjust the train timetables for large URT networks under the LPF events.

Dynamic modeling and online monitoring of tensor data streams with application to passenger flow surveillance

Dynamic modeling and online monitoring of tensor data streams with application to passenger flow surveillance

Research On the Design of Public Transit Services for The Elderly in China

As China enters an aging society, the elderly in China are increasingly becoming an important user group of public transportation. This study uses service design thinking to optimize the inclusiveness of Chinese elderly people's travel by bus and subway, and explores the feasibility of applying service design thinking to the field of public transportation. This paper adopts the observation method, with Guangzhou and Beijing as the research areas. The researchers conduct service design research based on the entire process of elderly travel, summarize the travel needs and pain points of elderly passengers, optimize the station guidance system, intelligent interface and service platform, make the public transportation service environment more friendly, and thus make the public transportation service environment more inclusive. Analyze the passenger flow, contact points, and stakeholders of the station, draw character portraits and journey maps, and output service design strategies to improve the travel experience of Chinese elderly passengers. In summary, this study aims to improve the convenience of Chinese elderly passengers taking public transportation and provide theoretical support for the transformation and optimization of China's public transportation services.

Method for Coordinated Passenger Inflow Control Strategy of Metro with Flexible Control Time Step Considering Long–Short Route Pattern

Method for Coordinated Passenger Inflow Control Strategy of Metro with Flexible Control Time Step Considering Long–Short Route Pattern

Resilience analysis of metro stations integrating infrastructures and passengers

Metro stations are important infrastructures ensuring people's daily commuting with tremendous travels every day, but susceptible to disruptions posing challenges in the resilience of the metro system. Previous studies mainly contributed to the resilience of the whole metro network, neglecting the resilience methodology and analysis of important stations. Integrating the infrastructure characteristics and the evolvement of passengers, a metro station resilience analysis methodology is developed. The approach is demonstrated with the stations of the metro system of Xi'an, China. Results show that station resilience varies differently across stations dominated by infrastructure types and queuing delay. Based on the comparison analysis under different infrastructure failures, failures of auto-ticket gates would have the highest impact on station resilience. When incidents on one infrastructure last for a period of time over 15 min, the resilience of station would be reduced by 9.8%, and dramatic resilience reductions could be observed if there are short-time incidents within 5 min on more than five infrastructures of a station. The findings would have practical significance for the resilience improvement of metro stations in infrastructure planning and passenger flow control against metro incidents.