Modeling of urban bus drivers thermal sensation vote as a function of the thermal comfort parameters

Matheus das Neves Almeida; Antonio Augusto de Paula Xavier; Ariel Orlei Michaloski

doi:10.31686/ijier.vol10.iss7.3811

Authors

Matheus das Neves Almeida

Universidade Federal do Piauí

https://orcid.org/0000-0001-8302-9295
Antonio Augusto de Paula Xavier

Universidade Tecnológica Federal do Paraná
Ariel Orlei Michaloski

Universidade Tecnológica Federal do Paraná

DOI:

https://doi.org/10.31686/ijier.vol10.iss7.3811

Keywords:

thermal comfort, thermal sensation vote, bus drivers, public transport

Abstract

Research into thermal comfort in vehicle environments has been gaining prominence among researchers due to the impacts generated, which range from maintaining the thermal sensation of the occupants, to ensuring the satisfactory performance of drivers in terms of safety in traffic and in energy sustainability. With this background, this study aimed to evaluate the thermal comfort parameters that influence the thermal sensation of urban bus drivers. To this mean, the four environmental parameters in the cabins of urban buses were measured and the two personal parameters of three drivers of the same bus line were estimated, and the influences of these six parameters on the subjective thermal sensation were analyzed using the Ordinal Logistic Regression Models of the Generalized Linear Models methodology. The field survey was performed from September to December 2021 and over three daily trips, totaling 180 measurements of thermal conditions. As a result, both the Predicted Mean Vote index and the thermal sensation votes indicate that the environments of the bus drivers' cabins analyzed are, in general, within the scale of thermal discomfort by heat, with a predominance of the "Warm" class. Furthermore, the model adjustments converged on only three distinct models and they demonstrated that the thermal sensation was influenced by the environmental parameters, and not by the personal parameters. Finally, we concluded that the model that best fit to the sensation was that as a function of the air temperature, with a moderate explanatory ability due to the value of Pseudo R² = 0.669. In addition, the proportional chance curves of this model indicated the following air temperature ranges for the respective heat thermal discomfort classes: when ta < 28°C, the greater chances are in the choice of thermal neutrality and the other classes of thermal discomfort by cold that were not reached by this research, which were not achieved by this research; for 28°C ≤ ta ≤ 30°C the tendency is higher for a slightly warm sensation; for values in the range 30.5°C ≤ ta ≤ 32.5°C it is more natural that they opine on the heat scale; and for values of ta > 33°C the tendency is for conductors to feel extremely hot.

References

Abreu, M. N. S., Siqueira, A. L., & Caiaffa, W. T. (2009). Regresión logística ordinal en estudios epidemiológicos. Revista de Saúde Pública, 43(1), 183–194. DOI: https://doi.org/10.1590/S0034-89102009000100025

Alahmer, A., Mayyas, A. A. A., Mayyas, A. A. A., Omar, M. A., & Shan, D. (2011). Vehicular thermal comfort models; a comprehensive review. Applied Thermal Engineering, 31(6–7), 995–1002. https://doi.org/10.1016/j.applthermaleng.2010.12.004 DOI: https://doi.org/10.1016/j.applthermaleng.2010.12.004

Almeida, M. das N., Xavier, A. A. de P., & Michaloski, A. O. (2020). A Review of Thermal Comfort Applied in Bus Cabin Environments. In Applied Sciences (Vol. 10, Issue 23). https://doi.org/10.3390/app10238648 DOI: https://doi.org/10.3390/app10238648

Almeida, M. das N., Xavier, A. A. de P., Michaloski, A. O., & Soares, A. L. (2020). Thermal Comfort in Bus Cabins: A Review of Parameters and Numerical Investigation. In Occupational and Environmental Safety and Health II (pp. 499–506). Springer. DOI: https://doi.org/10.1007/978-3-030-41486-3_54

ASHRAE - 55. (2017). Thermal Environmental Conditions for Human Occupancy, 2017. In American Society of Heating, Refrigerating and Air-conditioning Engineers. American Society of Heating, Refrigerating and Air-conditioning Engineers.

Assuncao, A., Jardim, R., & De Medeiros, A. (2014). Voice complaints among public transport workers in the metropolitan region of belo horizonte, Brazil. Folia Phoniatrica et Logopaedica, 65(5), 266–271. https://doi.org/10.1159/000357301 DOI: https://doi.org/10.1159/000357301

Cooper, D. R., & Schindler, P. S. (2014). Business Research Methods.© The McGraw− Hill Companies.

Croitoru, C., Nastase, I., Bode, F., Meslem, A., & Dogeanu, A. (2015). Thermal comfort models for indoor spaces and vehicles - Current capabilities and future perspectives. Renewable and Sustainable Energy Reviews, 44, 304–318. https://doi.org/10.1016/j.rser.2014.10.105 DOI: https://doi.org/10.1016/j.rser.2014.10.105

Danca, P., Vartires, A., & Dogeanu, A. (2016). An Overview of Current Methods for Thermal Comfort Assessment in Vehicle Cabin. Energy Procedia, 85(November 2015), 162–169. https://doi.org/10.1016/j.egypro.2015.12.322 DOI: https://doi.org/10.1016/j.egypro.2015.12.322

Djongyang, N., Tchinda, R., & Njomo, D. (2010). Thermal comfort: A review paper. Renewable and Sustainable Energy Reviews, 14(9), 2626–2640. https://doi.org/https://doi.org/10.1016/j.rser.2010.07.040 DOI: https://doi.org/10.1016/j.rser.2010.07.040

Enescu, D. (2017). A review of thermal comfort models and indicators for indoor environments. Renewable and Sustainable Energy Reviews, 79, 1353–1379. https://doi.org/https://doi.org/10.1016/j.rser.2017.05.175 DOI: https://doi.org/10.1016/j.rser.2017.05.175

Harrell, F. E. (2015). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis (Vol. 3). Springer. DOI: https://doi.org/10.1007/978-3-319-19425-7

Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons. DOI: https://doi.org/10.1002/9781118548387

Ismail, A. R., Abdullah, S. N. A., Abdullah, A. A., & Deros, B. M. (2015). A descriptive analysis of factors contributing to bus drivers’ performances while driving: A case study in Malaysia. International Journal of Automotive and Mechanical Engineering, 11(1), 2430–2437. https://doi.org/10.15282/ijame.11.2015.23.0204 DOI: https://doi.org/10.15282/ijame.11.2015.23.0204

Ismail, A. R., Atikah Abdullah, S. N., Abdullah, A. A., Ab Hamid, M. R., & Md. Deros, B. (2015). Relationship between thermal comfort and driving performance among Malaysian bus driver. ARPN Journal of Engineering and Applied Sciences, 10(17), 7406–7411.

ISO 7726. (1998). Ergonomics of the Thermal Environment: Instruments for Measuring Physical Quantities. In International Standard for Organization (Vol. 7726). International Organization for Standardization.

ISO 7730. (2005). Ergonomics of the thermal environment, analytical determination and interpretation of thermal comfort using calculations of the PMV and PPD indices and local thermal comfort criteria. In International Standard for Organization. International Organization for Standardization.

ISO 9920. (2007). Ergonomics of the thermal environment–Estimation of thermal insulation and water vapour resistance of a clothing ensemble. In International Standard for Organization. International Organization for Standardization.

King, B. M., Rosopa, P. J., & Minium, E. W. (2018). Statistical reasoning in the behavioral sciences. John Wiley & Sons.

Nguyen-Phuoc, D. Q., Currie, G., De Gruyter, C., Kim, I., & Young, W. (2018). Modelling the net traffic congestion impact of bus operations in Melbourne. Transportation Research Part A: Policy and Practice, 117, 1–12. https://doi.org/10.1016/j.tra.2018.08.005 DOI: https://doi.org/10.1016/j.tra.2018.08.005

Nguyen-Phuoc, D. Q., Currie, G., De Gruyter, C., & Young, W. (2018). Congestion relief and public transport: An enhanced method using disaggregate mode shift evidence. Case Studies on Transport Policy, 6(4), 518–528. https://doi.org/10.1016/j.cstp.2018.06.012 DOI: https://doi.org/10.1016/j.cstp.2018.06.012

Pala, U., & Oz, H. R. (2015). An investigation of thermal comfort inside a bus during heating period within a climatic chamber. Applied Ergonomics, 48, 164–176. https://doi.org/https://doi.org/10.1016/j.apergo.2014.11.014 DOI: https://doi.org/10.1016/j.apergo.2014.11.014

Peeters, L., De Dear, R., Hensen, J., & D’haeseleer, W. (2009). Thermal comfort in residential buildings: Comfort values and scales for building energy simulation. Applied Energy, 86(5), 772–780. DOI: https://doi.org/10.1016/j.apenergy.2008.07.011

Prakash, N. K. U., Bhuvaneswari, S., Kumar, M. R., Lankesh, S., & Rupesh, K. (2014). A study on the prevalence of indoor mycoflora in air conditioned buses. Microbiology Research Journal International, 282–292. DOI: https://doi.org/10.9734/BMRJ/2014/5380

Rupp, R. F., Vásquez, N. G., & Lamberts, R. (2015). A review of human thermal comfort in the built environment. Energy and Buildings, 105, 178–205. https://doi.org/https://doi.org/10.1016/j.enbuild.2015.07.047 DOI: https://doi.org/10.1016/j.enbuild.2015.07.047

Simion, M., Socaciu, L., & Unguresan, P. (2016). Factors which Influence the Thermal Comfort Inside of Vehicles. Energy Procedia, 85, 472–480. https://doi.org/https://doi.org/10.1016/j.egypro.2015.12.229 DOI: https://doi.org/10.1016/j.egypro.2015.12.229

Sweeney, D. J., Williams, T. A., & Anderson, D. R. (2013). Estatística aplicada à administração e economia. São Paulo: CENGAGE Learning.

Tartarini, F., Schiavon, S., Cheung, T., & Hoyt, T. (2020). CBE Thermal Comfort Tool: Online tool for thermal comfort calculations and visualizations. SoftwareX, 12, 100563. DOI: https://doi.org/10.1016/j.softx.2020.100563

Williams, R. (2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The Stata Journal, 6(1), 58–82. DOI: https://doi.org/10.1177/1536867X0600600104

Xavier, A. A. de P. (2000). Predição de conforto térmico em ambientes internos com atividades sedentárias-Teoria física aliada a estudos de campo. Florianópolis: Universidade Federal de Santa Catarina.

Yao, R., Li, B., & Liu, J. (2009). A theoretical adaptive model of thermal comfort – Adaptive Predicted Mean Vote (aPMV). Building and Environment, 44(10), 2089–2096. https://doi.org/https://doi.org/10.1016/j.buildenv.2009.02.014 DOI: https://doi.org/10.1016/j.buildenv.2009.02.014