Indoor air quality and passenger thermal comfort in Beijing metro transfer stations

Wei, Liming; Xia, Haishan; Wang, Xianfeng; Dong, Qingfeng; Lin, Chunxiang; Liu, Xiaotong; Liang, Rui

doi:10.1016/j.trd.2019.102217

Cited by 24 publications

(7 citation statements)

References 40 publications

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“…In UMSs, indoor air temperature and relative humidity are the two main parameters used to control the indoor thermal environment [25,26]. For example, according to [27],…”

Section: Thermal Comfort Inside Metro Stationsmentioning

confidence: 99%

A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations

You

Zhang

et al. 2021

Renewable and Sustainable Energy Reviews

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“…In UMSs, indoor air temperature and relative humidity are the two main parameters used to control the indoor thermal environment [25,26]. For example, according to [27],…”

Section: Thermal Comfort Inside Metro Stationsmentioning

confidence: 99%

A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations

You

Zhang

et al. 2021

Renewable and Sustainable Energy Reviews

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“…3 In order to protect the health and comfort of passengers, it is necessary to monitor the thermal comfort of passengers in the underground train carriage environment. 4…”

Section: Introductionmentioning

confidence: 99%

“…3 In order to protect the health and comfort of passengers, it is necessary to monitor the thermal comfort of passengers in the underground train carriage environment. 4 Thermal comfort is often defined as 'the state of mind that expresses satisfaction with the thermal environment', 5 and it can be divided into outdoor thermal comfort 6 and indoor thermal comfort. 7 We checked the relevant literature review and found that the studies on indoor thermal comfort could be divided into thermal comfort of buildings and occupant-centred thermal comfort.…”

Section: Introductionmentioning

confidence: 99%

Using random forests to predict passengers’ thermal comfort in underground train carriages

Huang

et al. 2022

Indoor and Built Environment

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This research developed an intelligent ensemble machine learning prediction model for the thermal comfort of passengers inside the compartment of the subway. Data sources used for data-driven modelling were obtained from on-site measurements and passengers’ questionnaires in the compartments of the Nanjing subway. The four models were established using methodologies of Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT) in machine learning, respectively. The performance of the RF method was compared with DT, LR and SVM in terms of conventional statistical metrics, namely, Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Correlation Coefficients squares (R2). Thermal Sensation Vote with the seven-level indicator (TSV-7) and Thermal Sensation Vote with the three-level indicator (TSV-3) were employed to obtain passengers’ thermal comfort and evaluate the models’ predictions. In this study, the R2 value of the RF model is 0.6527 and 0.6607 for TSV-7 and TSV-3, which shows higher accuracy than DT, LR and SVM models in predicting the two kinds of Thermal Sensation Vote (TSV). The results show that the predictive performance of the proposed RF model is outstanding, and it can predict the TSV value of passengers inside the compartment of the subway more efficiently.

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“…With the rising demand for the subway and its influence on public health, the subway’s thermal environment has also attracted the attention of researchers [ 2 ]. Therefore, it is necessary to evaluate the thermal comfort of the underground train carriage to ensure a healthy and comfortable thermal environment [ 3 ], which will also facilitate determining an energy management scheme to minimize the energy consumption of the subway [ 4 ]. Typically built environments consist of indoor, outdoor, and transitional spaces [ 5 ]; however, the subway-built environment differs with high mobility, short passenger stay times, and high passenger density.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask

et al. 2022

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Wearable sensors and machine learning algorithms are widely used for predicting an individual’s thermal sensation. However, most of the studies are limited to controlled laboratory experiments with inconvenient wearable sensors without considering the dynamic behavior of ambient conditions. In this study, we focused on predicting individual dynamic thermal sensation based on physiological and psychological data. We designed a smart face mask that can measure skin temperature (SKT) and exhaled breath temperature (EBT) and is powered by a rechargeable battery. Real-time human experiments were performed in a subway cabin with twenty male students under natural conditions. The data were collected using a smartphone application, and we created features using the wavelet decomposition technique. The bagged tree algorithm was selected to train the individual model, which showed an overall accuracy and f-1 score of 98.14% and 96.33%, respectively. An individual’s thermal sensation was significantly correlated with SKT, EBT, and associated features.

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Indoor air quality and passenger thermal comfort in Beijing metro transfer stations

Cited by 24 publications

References 40 publications

A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations

A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations

Using random forests to predict passengers’ thermal comfort in underground train carriages

Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask

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