Metro Platform Research Articles

An Experimental Pilot Approach to Evaluate the Infrastructure Accessibility and Level of Service at Metro Station Platforms

Metro stations are essential for daily commuting, but overcrowding due to increased demand can severely impact infrastructure quality and passenger experience. The Level of Service (LOS), a key indicator of congestion, is influenced by factors such as density, flow, and speed, and poor LOS leads to issues like longer boarding times, overcrowded platforms, and reduced accessibility, especially for vulnerable populations. To address these challenges, the study explores innovative solutions to improve platform design infrastructure for better accessibility and LOS, aligning sustainable development goals to create safer, more inclusive transport systems. This study presents two strategies designed to reduce passenger congestion at Francia station on the Valparaíso metro platform infrastructures. The strategies, tested in experimental scenarios, showed minimal differences in boarding and alighting times, with less than a one-second average variation between the two. However, survey results revealed that passengers preferred the strategy “let passengers alight before boarding the train”, as it provided greater comfort and accessibility, reducing the number of passengers per door and improving the LOS from level C to B. Despite the minimal intervention in the experiments, the results suggest potential operational improvements. Future research will focus on measuring passengers’ emotional responses using psychophysiological data to further evaluate the suitability of the proposed strategies.

Research on metro platform width calculation considering the effective length of the boarding area

Research on metro platform width calculation considering the effective length of the boarding area

Investigating passenger behaviour on the metro platform with Wi-Fi location tracking data: a case study of Singapore

Investigating passenger behaviour on the metro platform with Wi-Fi location tracking data: a case study of Singapore

Modelling study on pedestrian evacuation dynamics considering exit selection behaviour under flood disaster

Flood spreading in metro stations is a dangerous hazard that frequently causes casualties and property losses. However, pedestrian evacuation under flood spreading scenarios has not been fully investigated. To fill this gap,we propose an extend Floor-Field model incorporating flood spreading and exit selection behaviour of individuals. In the model, the dynamic flood spreading process on metro platforms is expressed by using a simulation software named Mike21. The optimal exit selected by pedestrians in each step is decided on three factors, that is pedestrian density around the exit, pedestrian distance to the exit, and water depth at the exit. Meanwhile, the movement speed of pedestrain also changes with the water depth at each time step. Based on this, the transition probability to neighbor cell including static field, dynamic field, height field, and water flow field is calculated. This results in a flood evacuation model considering the pedestrian's behaviour when choosing an exit. The effects of flooding, pedestrian density, different exit selection behaviours, water flow field, and inlet flow on evacuation were thoroughly analysed. The analysis demonstrates that the likelihood of flooding significantly impacts the evacuation of pedestrians from the subway station; the more flooding is taken into account, the more influential the impact. In addition, the effect of inlet flow on evacuation is also noteworthy; considering exit selection behaviour can significantly increase the effectiveness of evacuation. The study's findings can be used to develop evacuation plans for flooded metro stations.

Estimating PM levels on an underground metro platform by exploring a new model-based factor research

Over recent decades, the adverse impacts of airborne particles on human health have received wide attention. Elevated PM concentrations on underground platforms might pose a significant public health issue within underground metro systems. This study explores the impact of introducing a new type of train on the concentration of airborne particles on an underground metro platform through statistical modelling, analyses interactions between various factors, and estimates air quality on underground platforms after introducing a new type of train. Based on the data from a long-term field measurement, a linear mixed model, the multi-factor interaction model, which is an expansion of a previous multi-factor model, explored the impacts of train operations, passenger flow, urban background PM levels, ventilation, nighttime maintenance work, and their interactions on hourly PM10, PM2.5, and PM1 values on the platform. The model results show a positive correlation between those factors and platform PM10, PM2.5 and PM1 values, with significant interactions among these factors. The new model has a higher estimate quality than the previous model. Based on the combination of the model and measurement results, the levels of underground PM decreased significantly after replacing the old type of trains with new ones.

Willingness-to-Comply Analysis of Incentive Mechanisms for Alleviating Local Congestion in Metro Waiting Areas

Effectively equilibrating passenger distribution on metro platforms and carriages is important for relieving local congestion. This paper explores the role of incentive mechanisms in encouraging passenger queuing behaviours. To quantitatively analyse passenger compliance with the policy, a questionnaire survey was conducted in Fuzhou, China. According to the preliminary analysis of the survey data, passengers have various moving distance preferences under the incentive scenarios, namely, no movement, smaller distance and greater distance. Additionally, this paper establishes a nested logit model that considers travel purposes and moving distances. The empirical results show that although monetary and point-system incentives can effectively enhance passenger compliance with transfer queue-positioning requirements, when the moving distance is very small, people pay less attention to rewards. Compared to those commuting on weekends, passengers commuting on weekdays comply with policies more strongly, and the effect of implementing incentive policies is better; however, the effect of those policies is reduced among those travelling for leisure. Meanwhile, when travelling for leisure, as the number of companions increases, people’s willingness to follow guidance on where to wait increases. According to the results, the implementation of incentive-based waiting encouragement policies during peak working days can result in good compliance.

Risk analysis of crowd gathering on metro platforms during large passenger flow

With the continuous development of urbanization, metro systems have become an essential means of travel for urban residents. However, the increase of passengers using the metro system also increases the risk of occupant instability and stampede, which is especially significant on metro platforms considering the mixed passenger flow of boarding, waiting and getting off. How to grasp the overcrowding of passengers and gathering risk on the metro platform in time is the critical problem of the current research. To solve the difficulties in acquiring the number of passengers at metro platforms, a 7-phase model is constructed to estimate the risk of crowd gathering in each platform according to the Origin-Destination (OD) data. The proposed model divides a travel process into 7 components in detail and evaluates the crowd-gathering risk accurately to the platform. The travel time of each passenger is calculated by the station structure parameters and pedestrian parameters. Then, the travel stages of passengers are acquired by the travel time. Thirdly, the number of passengers on any platform and at any time is calculated. Finally, the temporal and spatial distribution of crowd-gathering risk on all metro platforms of a metro network is analyzed. The constructed passenger travel model and the crowd-gathering risk analysis method can effectively identify the high-risk platform and high-risk periods, which provides method and data support for metro operation safety improvement and crowd accident prevention.

Kinetic modeling of a leader–follower system in crowd evacuation with collective learning

A kinetic modeling of crowd evacuation with leaders and followers is considered in this paper, in which the followers may not know the full information about the walking environment and the evacuation strategy, but they follow the leaders and learn the walking strategy to get out of the walking venue. Based on the kinetic theory of active particles, the learning dynamics are considered by introducing an activity variable [Formula: see text], which represents the learning level of the followers and measures how much knowledge a follower has learned about the walking strategy, the walking environment, or the geometry of the walking venue. Several fundamental factors are considered in this leader–follower learning system of crowd evacuation, including: (1) the rational motion of all pedestrians, i.e. the trend to the exit or to a preferred direction, the ability to avoid collisions with walls or obstacles, and the tendency to search for less crowded direction with minimal density gradient, (2) the irrational motion of followers to follow other pedestrians induced by panic, (3) the learning dynamics of the followers who learn the walking strategy during interaction with others, and, (4) the transition from a follower to a leader when one’s activity reaches the highest level of learning. A numerical comparison of a metro platform evacuation with and without learning shows a reasonably good predictive ability of the model that the learning effect plays a significant role in the evacuation dynamics.

The Prediction of Evacuation Efficiency on Metro Platforms Based on Passengers’ Decision-Making Capability

In the research, decision-making capabilities are explored in relation to the prediction of evacuation efficiency to improve forecast accuracy on metro platforms. For this purpose, this study reviewed theories related to evacuation behaviours utilising the anomaly-seeking approach and the paradigm of relationship development. The conceptual framework of decision-making capability and evacuation behaviours was explored based on risk perception, level of emergency knowledge, survivability and emotion, and their relationship with the partial least squares equation was constructed. A predictive model of evacuation efficiency and its differential equations incorporating this relationship were also proposed based on the epidemic model. By developing and testing the conceptual framework and model, theoretical support is provided for evacuation behaviour, while assisting emergency management in developing plans and measures to respond to emergencies on metro platforms. This study realises the possibility of predicting evacuation efficiency from a decision-making capability perspective.

Evaluation of comfort perception of passengers in urban underground metro stations

Indoor thermal comfort levels in underground metro platforms and trains in Chennai metropolitan city are assessed in the present research study to determine the comfort perception of passengers. The field measurements were conducted in 7 underground metro stations during summer (Mar-Jun 2019) and winter seasons (Dec 2019 - Feb 2020). The thermal comfort of metro passengers is investigated through environmental parameters and a subjective survey. Results indicated that the percentage of unacceptability in metro platforms is in the range of 36.7–98.8%, while inside the metro trains it is less than 12%. This indicates the efficiency of the in-train air-conditioning systems compared to metro platforms. Good agreement is found between the findings of the thermal comfort investigation and the comfort perception of passengers. A linear and very strong positive correlation is found between operative temperature and the percentage of passengers dissatisfied. No relation is found between the metro stations' indoor and outdoor thermal conditions. Thus, it is inferred that internal factors such as the functioning of stations, operation of metro trains, and high density of passengers contribute significantly to indoor heat generation. During both seasons, the thermal conditions in all metro stations surpassed the ASHRAE-55 and ISO7730 guidelines. Stations with low passenger traffic are maintained with high operative temperatures to save energy costs. Based on the prevailing thermal conditions and ASHRAE recommendations of operative temperature, a Predicted Percentage of Dissatisfied (PPD) value of up to 40% could be deemed a thermal comfort metric that can be acceptable in underground metro stations.

Modeling and estimating noise pollution on metro platforms of Darvazeh Doulat and Tajrish Stations in Tehran metropolis

سابقه و هدف: مشکل آلودگی صدا جدی­ ترین مسئله ­ای است که بیشتر مردم به ­صورت عمومی با مقادیر بیش از حد مجاز آن، مواجه می ­باشند. هدف این پژوهش، بررسی میزان آلودگی صوتی حاصل از تردد قطار مترو شهری تهران در زمان ورود و خروج در ایستگاه­ های زیر زمینی دروازه دولت و تجریش می ­باشد.مواد و روش ها: در این پژوهش که در ماه­ های تابستان و پاییز سال 1398 روی ایستگاه ­های دروازه دولت و تجریش از خط یک متروی تهران صورت گرفته است، مقادیر بیشینه و کمینه شدت صوت، در عرض سکو در فاصله­ های معین از لبه سکو و در دو نوبت صبح و عصر در فاصله زمانی هر سه روز یکبار، به تعداد 48 ایستگاه اندازه گیری شده و تحلیل شده است. در ادامه تحقیق در ایستگاه تجریش با انجام برداشت­ های محدود با استفاده از شبکه عصبی مصنوعی به تخمین توزیع آلودگی صوتی پرداخته شده است.نتایج و بحث: مقادیر اندازه ­گیری شده در فاصله ­های معین از لبه سکو نشان می ­دهد که میزان آلودگی صدای حاصل از ورود قطار به سکو، ورود همزمان دو قطار به سکو، ورود قطار بدون توقف و عبور از سکو در بیشتر زمان ­ها، بالاتر از حد استاندارد بوده و در برخی ایستگاه ­های برداشت، به بالای 100 دسی بل هم می ­رسید که این خود نشان دهنده شرایط بحرانی آلودگی صوتی در این سکوهای مترو می ­باشد.نتیجه ­گیری: روش ­های مورد استفاده در تحقیق حاضر به ­منظور تخمین میزان آلودگی صوتی در سکوهای مترو کارآمد بوده است. به­ طوری که می ­توان با شناسایی محدوده ­های بحرانی، با ارائه تمهیداتی صدمه ­های حاصل از آلودگی صدا بر سلامت شهروندان را مدیریت نمود.

PM levels on an underground metro platform: A study of the train, passenger flow, urban background, ventilation, and night maintenance effects

The negative effect of airborne particles on human beings has been widely noticed in recent decades. The high particle mass concentrations on underground platforms can be a significant public health issue for underground metro systems. This study monitored PM10, PM2.5, and PM1 particle mass concentrations in a field measurement performed from January to December 2020 on an underground metro platform in Stockholm, Sweden. A linear mixed model, named the multi-factor model, was set up based on the measurement results. The model contains the commuting effects, which are train frequency, train type and passenger flow, and the non-commuting effects, which are urban-background air quality, ventilation, night maintenance, and the accumulation effect in an hourly step. The R 2 values of the multi-factor model when estimating platform hourly PM10, PM2.5, and PM1 levels are 0.89, 0.88 and 0.86. All factors in the model are statistically significant. According to the model, the train frequency, train type, passenger flow, urban background air quality, and night maintenance positively correlate with the platform PM concentrations. The train frequency effect is more significant for PM10 particles than the train type effect, and the train type effect is more evident for PM1 particles. The passenger flow effect contributes more to the PM1 levels than other coarser particles. The ventilation system can notably decrease the platform PM concentrations for all particle sizes. • A multi-factor linear mixed model was set up based on the field measurement data. • The train frequency effect is more evident for coarse particles than finer ones. • Train type effect contributes more to platform fine particle mass concertation. • Passenger, urban PM, and maintenance have positive correlations with platform PM. • A passive ventilation system can decrease the platform PM levels by around 20%.

Can Monetary Incentive Regulate Boarding Passenger Distribution on the Metro Station Platform?

To equilibrate the passenger distribution on the metro platform and carriage, a monetary incentive policy was explored in this paper; a discount on travel fare was provided to motivate metro passengers to queue for boarding in the noncrowded areas on the platform. The congested state is evaluated combined with the passenger distribution in the upcoming metro carriage. The utility of metro passengers and companies caused by the monetary incentive policy was analyzed, and the binary logit model was used to relate the utility to the passenger’s willingness to move from crowded areas to noncrowded ones. With data acquired from the questionnaire survey, a regression analysis was employed to explain the variation in passengers’ willingness to move as a function of discount level as well as personal and trip characteristics. The regression results show that effect of incentive discount is greater on female passengers and elderly passengers. A 10% discount can motivate most passengers aged over 40, and a 30% discount works on most female passengers. According to the different levels of passenger sensitivity, a particular discount can be determined to motivate a specific proportion of passengers to move and achieve the regulation of passenger distribution on the metro station platform and metro carriage.

PM10 in underground stations from different types of trains

• A positive correlation between hourly train frequency and PM10 level is verified. • The type of train passing an underground metro platform affects the PM10 level. • In Stockholm metro, the PM10 could be reduced by up to 30% by train type selection. In underground metro systems, airborne wear particles generated during the operation of metro trains are the predominant airborne contaminants on platforms. Previous researchers have found that several factors influence the airborne particle emission rate during metro train operations. However, the effect of train type has rarely been studied. In 2016 and 2020, field measurements were taken on six underground metro platforms in Stockholm, Sweden. Old and new types of trains (CX and C20) were in traffic at the same time. In the present study, based on the field measurements, the correlations between the hourly average PM10 level and the hourly frequency of the two types of trains were studied. A linear mixed model that includes the effect of hourly train frequency and train type was set up. Based on this model, CX trains have a larger particle emission rate than C20 trains.

Convolutional Neural Network for Crowd Counting on Metro Platforms

Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.

Timetable Optimization for Metro Lines Connecting to Intercity Railway Stations to Minimize Passenger Waiting Time

In major metropolitan cities, metro and intercity rail services usually share stations or have stations in close proximity to facilitate smooth connections for passengers. A large number of passengers may arrive on metro platforms within a short period of time upon the arrivals of intercity trains, while the number may drop significantly afterward. This injected passenger demand is not coped well by the metro timetable with the commonly adopted regular train headway. Focusing on metro lines connecting to intercity railways, this study puts forward a timetable model to optimize train headways at each station, assuming all passengers board the first train available. The objective is to minimize the total waiting time of passengers at platforms throughout the metro line, without changing the number of vehicle trips. To calculate the passenger waiting time accurately, a mathematical model is proposed to obtain the number of transfer passengers arriving at metro platform in each time interval, based on arrivals of intercity trains and transfer facilities at the station. A genetic algorithm, combined with interior-point algorithm, is developed to obtain the solution of the proposed timetable model. Real-life case studies show that the optimized timetable reduces the total passenger waiting time when compared with the current timetable and the solution of another model optimizing train departure times at the first station only. In addition, the peak number of passengers congregating at metro platforms drops significantly, which relieves passenger crowding on the platform.

Numerical simulation of the fire emergency evacuation for a metro platform accident

A coupled analysis of agent behavior and Computational Fluid Dynamics (CFD) model is applied to investigate the fire evacuation effectiveness in a popular metro station in Guangzhou, China. Due to the high density and complexity of traffic, the concept of Required Safe Escape Time and Available Safe Escape Time (RSET/ASET), which is more flexible and adaptable than the “6 minutes” principle, is applied in the safety assessment of fire evacuation. To pursue a stable simulation of the coupled model, the standard Critical Radiant Flux is used to deter the tenability criteria for exposure to fire and heat. Various related factors, including the fire location, the Heat Release Rate (HRR) of fire, the crowd density, and the operation mode of escalators, are analyzed through a series of simulations. Results indicate that the interaction between fire and humans should not be neglected in the evacuation simulation. Both the fire location and the crowd density have a significant effect on the evacuation, while the HRR of fire has a minor impact. When the accident happens at the entrance of an escalator, RSET is 58.3% longer than that when the accident occurs in the middle of the platform. RSET grows with the increase of the crowd density linearly. Besides, the evacuation efficiency could be partly improved by changing escalators that usually operate in the descending mode into ascending mode.

A study on passengers’ alighting and boarding process at metro platform by computer simulation

Abstract Passengers’ alighting and boarding process in metro stations has attracted increasing research attention since it has significant influence on the platform passenger distribution as well as the train dwell time. In this study, a field survey was firstly performed in a densely populated metro station at downtown area in Hong Kong to observe passengers’ alighting and boarding characteristics and collect sample data. One commonly existing phenomenon is found that boarding passengers start to get aboard even when there are alighting passengers still inside the metro carriage. This is defined as passengers’ non-compliance behaviors in this paper. In addition, time indicators are defined to measure the alighting and boarding efficiency. Then a microscopic pedestrian simulation model based on the Social Force Model is proposed to simulate the passengers’ alighting and boarding patterns at metro platform. The verification result shows the good applicability of the proposed model to simulate the actual situation. Finally, several simulation tests are conducted to explore the impacts that passengers’ non-compliance behaviors have on the alighting and boarding efficiency in different passenger volume conditions. The simulation result shows that higher level of passengers’ non-compliance behaviors leads to longer passenger’s alighting duration and boarding duration, but the influence on the overall transaction time is related to different passenger volume conditions. Thus, metro station facility could apply different alighting and boarding rules in different passenger volume conditions to increase alighting and boarding efficiency.

Metro Scheduling to Minimize Travel Time and Operating Cost Considering Spatial and Temporal Constraints on Passenger Boarding

Passengers on metro platforms can board a train only when the train has surplus capacity and the dwell time is sufficient, while the latter condition is omitted in previous studies. Taking into account the impacts of train capacity and dwell time on passengers boarding, this study develops a model on optimizing metro timetable to reduce passenger travel time and metro operating cost, through regulating trains' inter-station run-time, dwell time and headway. The NSGA-II algorithm is employed to obtain the near-optimal Pareto Frontier of the proposed model. To address insufficient dwell time scheduled in the timetable, three operating strategies are proposed and compared: a. sticking to nominal timetable; b. extending dwell time only; c. extending dwell time and recovering delay as soon as possible by compressing train inter-station run-time. Case studies on real-life metro line prove that some passengers cannot board the train during peak hours due to insufficient dwell time. In this context, strategy a brings low-quality service because passengers are stranded at platform even though the train has surplus capacity. In contrast, more passengers can board the train with strategies b and c because dwell time is extended for passengers' boarding when train has surplus capacity. Compared to strategy b, strategy c reduces the average in-vehicle time of passengers by 2.5% through compressing inter-station run-time to recover the delay. The timetable optimized based on strategy c saves total travel time of passengers by 3.1% without increasing operating cost when compared to the practical timetable.

An Integrated Cellular Automata Approach for Spatial Evacuation Simulation on Metro Platforms with Smoke Spreading

To simulate pedestrian evacuation processes on a metro station platform in a case of fire, a specific evacuation model is proposed, using an integrated cellular automata (CA) approach, in which the impacts from exits, other evacuees, and fire and smoke are included to measure the probability of the evacuee getting to each neighboring cell. The evacuation is firstly identified as a two-stage process including the motion on the platform and on the treads. Then the evacuation space is drawn to be a three-dimensional grid space, in which the cell size is defined by the stair structure and human body size. Based on that, this study proposes two CA models to simulate the evacuees’ movement and the smoke diffusion separately. Moreover, to describe the evacuation process in detail, the evacuation model is modified in three ways. First, transition rules in the evacuees’ movement model are embedded by social force theory to measure the impacts from the environment. Second, the smoke diffusion process is modified by considering the smoke control measures on the metro platform. Third, impact from smoke is quantified by the proportion of smoke in the centroid cell of evacuees. Finally, results from simulation experiments show that this model is able to recognize the arching and stagnation phenomenon at the foot of staircases, and the relations between the evacuation time and the crowd density for different parameters are also analyzed. The proposed method of simulating the pedestrian evacuation process can be useful in providing guiding principles for the software design of evacuation in metro systems.