Subway Station Platform Research Articles

Analysis of Inundation Flow Characteristics and Risk Assessment in a Subway Model Using Flow Simulations

Subway station platforms are vulnerable to flood damage. Thus, an investigation of inundation in subway platforms is required to ensure the safety of citizens against flooding. This study analyzed and validated the inundation characteristics and safety areas in a subway station model using experimental inundation depth measurements and numerical simulations. Then by using the simulation, the effects of increased inflow to water velocity and depth were analyzed, and its impact on human models was found by using risk assessments which included specific force (M0), Flood Hazard Degree (FD), Flood Intensity Factors (FIF), toppling velocity, and sliding velocity. The flood risk assessment analysis results show that assessments using M0 could increase uncertainty by broadening the evaluation of risky areas compared to other indices. Also, the drag force applied to the human models was calculated using the simulations, which provided inundation risk values to people in subway stations. Overall, the risk assessments would provide a criterion for flood situations in subway stations.

Ferruginous components of particulate matters in subway environments, α-Fe2O3 or Fe3O4, exacerbates allergies

The respiratory effects of particulate matter (PM) in subway station platforms or tunnels have attracted considerable research attention. However, no studies have characterized the effects of subway PM on allergic immune responses. In this study, iron oxide (α-Fe2O3 and Fe3O4) particles—the main components of subway PM—were intratracheally administered to BALB/c mice where ovalbumin (OVA) induced allergic pulmonary inflammation. Iron oxide particles enhanced OVA-induced eosinophil recruitment around the bronchi and mucus production from airway epithelium. The concentrations of type 2 cytokines, namely, interleukin (IL)-5 and IL-13, in bronchial alveolar lavage fluids were increased by iron oxide particles. Iron oxide particles also increased the number of type 2 innate lymphoid cells and CD86+ cells in the lung. Moreover, phagocytosis of particles in lung cells was confirmed by Raman spectroscopy. In a subsequent in vitro study, bone marrow-derived antigen-presenting cells (APCs) isolated from NC/Nga mice were exposed to iron oxide particles and OVA. They were also exposed to outdoor ambient PM: Vehicle Exhaust Particulates (VEP) and Urban Aerosols (UA) as references. Iron oxide particles promoted the release of lactate dehydrogenase, C-X-C motif chemokine ligand 1 and IL-1α from APCs, which tended to be stronger than those of VEP. These results suggest that iron oxide particles enhance antigen presentation in the lungs, promoting allergic immune response in mice; iron oxide particles-induced death and inflammatory response of APCs can contribute to allergy exacerbation. Although iron oxide particles do not contain various compounds like VEP, iron oxide alone may have sufficient influence.

Dynamic evacuation path planning for subway station fire based on IACO

Fire in subway stations is the most serious type of accident causing casualties, and traditional static evacuation paths are no longer able to guarantee evacuation safety. To solve this problem, this study proposes an improved ant colony algorithm (IACO), which dynamically adjusts the evacuation paths by combining the simulation results of simulation software (PyroSim), and extends the available safe evacuation time of the personnel in a limited way. First, PyroSim is used to simulate the subway station hall and platform fire scenarios with the temperature, carbon monoxide (CO) concentration, and visibility at each location, obtained from twelve sets of monitors. Improvements in the pheromone concentration and update strategy of the ant colony algorithm (ACO) can improve the accuracy and convergence speed of the algorithm. Optimal path planning is achieved by the IACO, and then evacuation paths are dynamically adjusted by comparing the time at which each monitoring point on the path reaches the human hazard thresholds. Finally, until no adjustments can be made to obtain an evacuation path that extends the available safe evacuation time for personnel in a limited way. Compared to the traditional static evacuation path, this study provides a dynamic evacuation path that takes into account the real-time changes of fires in subway stations. This dynamic evacuation path planning approach will be put into use in subway stations before the accident to provide guidance on fire safety in subway stations and to provide contingency plans for emergency evacuation.

Sensitivity Analysis of Influencing Factors of Fire Smoke Transport on Subway Station Platforms

Sensitivity Analysis of Influencing Factors of Fire Smoke Transport on Subway Station Platforms

Fire evacuation integration modelling in subway station using the lattice Boltzmann method

Fire evacuation simulations are essential for assessing evacuation schemes in subway stations. However, most studies and practical solutions have primarily focused on the effects of fire smoke on the evacuation behaviour of evacuees. Moreover, these solutions overlook the effects of the movement of evacuees on fire-smoke diffusion; hence, they do not adequately reflect fire–pedestrian interaction. This study addresses this problem by formulating a fire-smoke evolution model using the lattice Boltzmann method to simulate fire smoke from a mesoscopic perspective. The proposed model can reflect fire-smoke diffusion, including the effects of evacuees on the evacuation scheme. The model easily exchanges data with the microscopic pedestrian simulation model. An agent-based evacuation model is formulated using the social force method. The agent can regulate the evacuation states using perceptive environment information. Finally, the models are integrated into one framework to evaluate fire–pedestrian interaction. A simplified fire evacuation scenario based on a two-dimensional subway station platform is simulated using the proposed integration model. The results show that the proposed integration model can simulate the effect of the disturbance of the movement of evacuees on fire-smoke diffusion and evaluate fire–pedestrian interaction.

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms.

Particulate matter (PM) of sizes less than 10 µm () and 2.5 µm () found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future and on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed and forecasting framework is demonstrated using comparisons with the different existing deep learning models.

An Automatic Control Algorithm for Air Flow Rate at a Subway Station Platform under the Impact of Piston Effect

An Automatic Control Algorithm for Air Flow Rate at a Subway Station Platform under the Impact of Piston Effect

Simulation study on the dynamic velocity field of the supplying jet coupled with the platform piston wind in non-screen door subway stations

The coupling airflows of platform piston wind and station air-conditioning supply air is the most typical airflow organization of non-screen door subway station platform. By establishing a CFD software model of the coupling process between piston wind and air-conditioning supply air in subway station platform, the velocity field of the coupled airflow in the platform under different vehicle conditions is numerically simulated. It has been found that: when the train enters the station, the maximal piston wind velocity can reach 8 m/s with the air-conditioning supply air near the approach end significantly affected by the piston wind than at other positions. The study can provide a theoretical basis for the optimization of the airflow organization and energy consumption in the non-screen door subway station platform.

An Automatic Control Algorithm for Air Flow Rate at a Subway Station Platform under the Impact of Piston Effect

An Automatic Control Algorithm for Air Flow Rate at a Subway Station Platform under the Impact of Piston Effect

Personnel identification and distribution density analysis of subway station based on convolution neural network

In this paper, a method based on convolution neural network and multi-camera fusion is proposed to improve the recognition accuracy of crowd and then the personnel distribution of subway station platform is analyzed. In this method, tensorflow is used as the deep learning training framework and the yolov4 neural network algorithm is used to identify the subway station platform area using three videos synchronously. Through affine transformation and time average statistics, the passenger density of each sub-area is calculated and the distribution of personnel density in the whole area is analyzed. The results show that the number of people recognized by multiple cameras is 58% higher than that by single camera. The new recognition method has high recognition rate for the actual scene with large crowd and more obstacles. Finally some areas with high risk of personnel aggregation have been found, which should be the focus of safety monitoring.

Emergency evacuation with unbalanced utilization of exits at platform level: A simulation study

Urban rail transit station has a large number of people gathering and relatively closed space, which has become a high incidence of emergencies. In the process of emergency evacuation, due to the unbalanced utilization rate of exits, the evacuation time is prolonged, which seriously threatens the safety of people in the station. In order to find out the reasons for the imbalance of the utilization rate of the exit, and put forward the solution, balance the utilization rate of each exit, improve the evacuation efficiency. In this paper, a subway station platform in Southwest China is taken as an example to establish a three-dimensional simulation model by using anylogic software. Through in-depth analysis, it is found that the reason for the unbalanced utilization of the platform floor exit is that the number of evacuation people at each exit does not match the evacuation capacity. According to the analysis results, the optimization model is put forward. Through comparative analysis, it is found that the optimization model can effectively balance the export utilization rate. Under the current conditions of off peak passenger flow, peak passenger flow and long-term peak passenger flow, the evacuation time can be saved by more than 20%.

Application of the Bayesian spline method to analyze real-time measurements of ultrafine particle concentration in the Parisian subway.

Air pollution in subway environments is a growing concern as it often exceeds WHO recommendations for indoor air quality. Ultrafine particles (UFP), for which there is still no regulation nor a standardized exposure monitoring method, are the strongest contributor to this pollution when the number concentration is used as exposure metric. We aimed to assess the real-time UFP number concentration in the personal breathing zone (PBZ) of three types of underground Parisian subway professionals and analyze it using a novel Bayesian spline approach. Consecutively, we investigated the effect of job, week day, subway station, worker location, and some further events on UFP number concentrations. The data collection procedure originated from a longitudinal study and lasted for a total duration of 6 weeks (from October 7 to November 15, 2019, i.e. two weeks per type of subway professionals). Time-series were built from the real-time particle number concentration (PNC) measured in the PBZ of professionals during their work-shifts. Complementarily, contextual information expressed as Station, Environment, and Event variables were extracted from activity logbooks completed for every work-shift. A Bayesian spline approach was applied to model the PNC within a Bayesian framework as a function of the mentioned contextual information. Overall, the Bayesian spline method suited a real-time personal PNC data modeling approach. The model enabled estimating the differences in UFP exposure between subway professionals, stations, and various locations. Our results suggest a higher PNC closer to the subway tracks, with the highest PNC on subway station platforms. Studied event and week day variables had a lesser influence. It was shown that the Bayesian spline method is suitable to investigate individual exposure to UFP in underground subway settings. This method is informative for better documenting the magnitude and variability of UFP exposure, and for understanding the determinants in view of further regulation and control of this exposure.

3D simulations of smoke exhaust system in two types of subway station platforms

In this study, smoke evacuation in two kinds of subway station platform models, namely: an island platform station and a two side platforms station is simulated in a three-dimensional configuration. FDS 6.1.1 (Fire Dynamic Simulator) software has been employed to perform numerical simulations. The results showed noticeable effects of platform architecture on the process of smoke evacuation and emission spreading. The location of the connecting stairs (station accesses) between floors in both types of subway stations, installing the walls around these stairs in island platform type, the presence or absence of fireproof and antismoke curtains in suitable areas in two side platforms type have significant impacts on the smoke spreading. At the end, after the comparison between smoke dispersion in these two types of subway's platform, different architecture corrections were suggested and evaluated. Overall, it is concluded that an island platform station is more challenging in smoke control point of view, because the openings at the ceiling of the platform (stairway path) provide a convenient space for smoke to escape to the upper floors. In summary station architecture and even the smallest architectural changes at a subway station can change the smoke dispersion in space.

Characterization of Urban Subway Microenvironment Exposure- A Case of Nanjing in China.

Environmental quality in public rail transit has recently raised great concern, with more attention paid to underground subway microenvironment. This research aimed to provide guidance for healthy urban subway microenvironments (sub-MEs) according to comprehensive micro-environmental categories, including thermal environment, air quality, lighting environment, and acoustic environment from both practical and regulation perspectives. Field sampling experiments were conducted in Nanjing Metro Line X (NMLX). Descriptive analysis, correlation analysis and one-way analysis of variance were used to investigate the status quo of urban sub-MEs. A paired samples t-test was then performed to compare among subway station halls, platforms, and in-cabin trains based on integrated sub-MEs. Results show that relative humidity, air velocity, respirable particulate matter (PM10) concentration, and illuminance dissatisfy the requirements in relevant national standards. Significant difference was observed in lighting environment between station hall and platform. It was detected platforms are warmer and more polluted than train cabins. Additionally, subway trains generate main noise on platform which is much louder when leaving than arriving. Protective strategies for sub-ME improvement as well as principles for updating standards were proposed from a proactive point of view. The findings are beneficial for moving towards healthy urban sub-MEs and more sustainable operation of subway systems.

Development of a magnetic hybrid filter to reduce PM10 in a subway platform.

This study investigated the reduction of particulate matter (PM) in a subway platform using self-developed magnetic hybrid filters (magnet-magnet (MM) and magnet-cascade (MC) filter). The magnetic hybrid filter systems were installed and operated in Jegi-dong subway station (J station) platform. The removal efficiency of PM10 (particular matter with aerodynamic diameter less than 10 μm) was evaluated according to various influencing factors such as the combination of filters, linear velocity, and operating conditions of trains. As a result, the average removal efficiency of the MC filter (40.5%) was higher than that of the MM one (27.0%). The maximum PM10 removal efficiencies by MM (34.1%) and MC (47.2%) filters were observed at 20 (linear velocity: 2.41 m/s) and 30 jog (8 m/s) dials, respectively. We additionally found that the removal efficiency of PM10 using MM and MC filters suddenly decreased when the concentration of background PM10 in the platform increased. Based on the results of this study, hybrid technology using two or more capture principles can remove PM more efficiently than technology using a single such principle.

Investigation of the Characteristics of Particulate Matter Suspended in a Subway Station Platform

Investigation of the Characteristics of Particulate Matter Suspended in a Subway Station Platform

Measuring Inundation Depth in a Subway Station Using the Laser Image Analysis Method

Subway station platforms are vulnerable to flood damage. Thus, investigation of inundation properties in subway platforms is required to ensure the safety of citizens against flooding. In this study, the evacuation time and safety were analyzed in a subway station model using inundation depth measurements. The subway station model contained shallow water depth conditions, which did not allow for contact-type measurement devices. Instead, an image analysis procedure using laser images was proposed to measure the inundation depth. The proposed laser image analysis method can recognize a boundary line between the water and air by visualizing the water surface using a laser sheet. The inundation depth measurements using the image analysis method were reasonably accurate, resulting in differences of 2.97–7.67% compared to the results obtained using a digital point gauge. When inflow positions and flowrates of rainwater were changed, the measured results showed that the inundation depth increased in areas in which the rainwater inflow was relatively small or collided when moving in the direction opposite to the rainwater. The calculated evacuation time from the subway station showed that a drainage system is required to decrease the inundation depth in areas of inflowing rainwater collision. Furthermore, the estimated results of evacuation safety showed that safety handles are necessary even in low depth regions to prevent people from falling down due to increased flow velocity, during evacuation.

Characteristics of PM10 and CO2 concentrations on 100 underground subway station platforms in 2014 and 2015

In this study, the concentrations of particulate matter 10 μm or less in diameter (PM10) and carbon dioxide (CO2) were measured in 100 underground subway stations, and the potential health risks of PM10, and environmental factors affecting these concentrations were analyzed. The concentrations were measured from May 2014 to September 2015 in stations along Seoul Metro lines 1–4. There were significantly different PM10 concentrations among the underground subway stations along lines 1, 2, 3, and 4. The PM10 concentrations were associated with the CO2 concentrations, construction years, station depths, and numbers of passengers. The underground PM10 concentrations were significantly higher than the outdoor PM10 concentrations. In addition, the PM10 concentrations were higher in the stations that were constructed in the 1970s than in those constructed after the 1970s. The PM10 and CO2 concentrations varied significantly, depending on the construction year and number of passengers. The hazard quotient is higher than the acceptable level of 1.0 μg kg−1 day for children, indicating that they are at risk of exposure to unsafe PM10 levels when travelling by the metro. Therefore, stricter management may be necessary for the stations constructed in the 1970s as well as those with higher numbers of passengers.

Commuter exposure to particulate matter for different transportation modes in Xi'an, China

Toxic air pollution on city streets is a very important issue, as pollutants are associated with adverse health effects, including respiratory and cardiovascular diseases. This study compared commuters' exposures to inhalable suspended particulate matter (PM) for different transportation modes in Xi'an City, China. Four commuting modes—private car, subway, bus and walking—were selected for the study. Commuter exposure concentrations to PM (PM10, PM2.5, PM1.0) were investigated in the following microenvironments: private cars under four ventilation modes, subway trains and station platforms, buses under two different ventilation modes, and pedestrians. Pearson correlation analysis was used to analyze the relationships between commuter PM10, PM2.5 and PM1.0 exposure concentrations under the different commuting modes. A mixed-effect linear model was used to identify the effects of different commuting modes on PM mass and number concentrations in these different traffic microenvironments. The results indicated that the concentration of particulate matter (PM) is significantly influenced by transportation mode as well as by vehicle ventilation systems. Among the four commuting modes, commuters were exposed to the lowest concentrations of PM10 (11.83 ± 7.60 μg m−3), PM2.5 (10.09 ± 6.63 μg m−3) and PM1.0 (9.52 ± 6.17 μg m−3) in a private car with air conditioning recirculation. In contrast, passengers waiting for a train on a subway station platform were exposed to the highest PM concentrations (244.99 ± 43.19 μg m−3). Size fractions of PM differed greatly across PM exposures with the ratio of fine particles to coarser particles (PM2.5/PM10) varying from 45 to 96%.

Physico-chemical characterization of PM2.5 in the microenvironment of Shanghai subway

The Shanghai subway metro system has brought great convenience to the city's travelling public, although passengers are exposed to airborne particles in this built micro-environment. However, investigations on the physicochemical characterization of PM2.5 air pollution in the Shanghai subway system are to date very limited. Three subway stations along the No. 7 line were selected as subway PM2.5 monitoring sites: Pan'guang, Shanghai University (SHU), and Jing'an, which are located in an outer suburban area, a suburban area and the urban area, respectively, airborne PM2.5 on the subway station platforms and in the ambient atmosphere above-ground was synchronously collected from 19th March to 4th, May, 2012. Cutting-edge techniques, including scanning electronic microscopy coupled with energy dispersive X-ray (SEM/EDX), inductively coupled plasma mass spectrometry (ICP-MS) and X-ray absorption near-edge structure (XANES) were employed to investigate microscopic characterization, chemical elements and speciation of the main heavy metals in subway PM2.5. Our results demonstrated that mass levels of PM2.5 in the subway stations were higher than that in ambient air. Mass levels of PM2.5 in the subway stations and in ambient air ranged from 49.17±19.7μg/m3 to 66.15±25.20μg/m3, and 24.52±3.3μg/m3 to 65.60±5.6μg/m3, respectively. The microscopic characterization of PM2.5 in ambient air and in subway stations showed marked differences. The PM2.5 in the subway stations was mainly composed of iron-containing particles and mineral particles, while the PM2.5 in ambient air largely consisted of mineral particles and soot aggregates. Fe was the most abundant element in subway PM2.5, followed by: major elements (mass level>100ng/m3) including Na, Mg, Al, K, Ca, Zn, Mn, Ba; sub-major elements (10ng/m3<mass level<100ng/m3) including Li, Cr, Ni,Cu, Ga, Sr, Pb; and minor elements (mass level<10ng/m3 ), Be, V, As, Se, Rb, Ag, Cd, Tl, Bi. The mass levels of Ca, Al and Zn in ambient PM2.5 were higher than those in subway PM2.5, however those of the remaining 26 measured elements in subway PM2.5 were higher than in ambient PM2.5. The speciation of Fe in PM2.5 was in the form of Fe2+, while for Cu, that in the finer fractions (<0.25, 0.5 and 1.0μm) was in the form Cu2+, but in the PM2.5 fraction itself, was as Cu1+.