Implementation of non-pharmaceutical intervention of COVID-19 in MRT through engineering controlled queue line using participatory ergonomics approach
Abstract
The viral transmission in public places and transportations can be minimized by following the world health organization (WHO) guideline. However, the uncertainty in a dynamic system complicates the social engagement to the physical distancing regulation. This study aims to overcome this obstacle in MRT stations and train by developing an adaptive queue line system. The system was developed using low-cost hardware and open-source software to guide passengers using visual information. The system works by capturing seat images and identify the presence of humans using a cloud machine learning service. The physical representation of MRT was translated to data representation using the internet of things (IoT). The data then streamed using an asynchronous API with a representative endpoint. The endpoint is then accessed by a display computer in the destination station platform to provide visual information. The visual information was ergonomically designed with visual display principles, including the minimum content load, layout, color combination, and dimension of contents. The design of the system was evaluated by Markov simulation of virus transmission in train and usability testing of the visual design. The implementation of the system has balanced the queue line capacity in station and crowd spots distribution in MRT. The system was effective due to the visual cortex manipulation by visual information. Consequently, the aerosol and falling droplets' viral transmission radius can be reduced. Accordingly, the chance for airborne transmission can be lowered. Therefore, the adaptive queue line system is a non-pharmaceutical intervention of viral transmission diseases in public transportation
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Zheng, R., Xu, Y., Wang, W., Ning, G., Bi, Y. (2020). Spatial transmission of COVID-19 via public and private transportation in China. Travel Medicine and Infectious Disease, 34, 101626. doi: https://doi.org/10.1016/j.tmaid.2020.101626
De Vos, J. (2020). The effect of COVID-19 and subsequent social distancing on travel behavior. Transportation Research Interdisciplinary Perspectives, 5, 100121. doi: https://doi.org/10.1016/j.trip.2020.100121
Gopalakrishnan, B., Peters, R., Vanzetti, D. (2020). Covid-19 and Tourism. UNCTAD. Available at: https://unctad.org/system/files/official-document/ditcinf2020d3_en.pdf
Sigala, M. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117, 312–321. doi: https://doi.org/10.1016/j.jbusres.2020.06.015
Dubey, S., Biswas, P., Ghosh, R., Chatterjee, S., Dubey, M. J., Chatterjee, S. et. al. (2020). Psychosocial impact of COVID-19. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14 (5), 779–788. doi: https://doi.org/10.1016/j.dsx.2020.05.035
Usher, K., Durkin, J., Bhullar, N. (2020). The COVID‐19 pandemic and mental health impacts. International Journal of Mental Health Nursing, 29 (3), 315–318. doi: https://doi.org/10.1111/inm.12726
Blume, C., Schmidt, M. H., Cajochen, C. (2020). Effects of the COVID-19 lockdown on human sleep and rest-activity rhythms. Current Biology, 30 (14), R795–R797. doi: https://doi.org/10.1016/j.cub.2020.06.021
Lakshmanaprabu, S. K., Shankar, K., Sheeba Rani, S., Abdulhay, E., Arunkumar, N., Ramirez, G., Uthayakumar, J. (2019). An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: Towards smart cities. Journal of Cleaner Production, 217, 584–593. doi: https://doi.org/10.1016/j.jclepro.2019.01.115
Tirachini, A., Cats, O. (2020). COVID-19 and Public Transportation: Current Assessment, Prospects, and Research Needs. Journal of Public Transportation, 22 (1). doi: https://doi.org/10.5038/2375-0901.22.1.1
Freedman, D. O., Wilder-Smith, A. (2020). In-flight transmission of SARS-CoV-2: a review of the attack rates and available data on the efficacy of face masks. Journal of Travel Medicine, 27 (8). doi: https://doi.org/10.1093/jtm/taaa178
Goscé, L., Johansson, A. (2018). Analysing the link between public transport use and airborne transmission: mobility and contagion in the London underground. Environmental Health, 17 (1). doi: https://doi.org/10.1186/s12940-018-0427-5
Mijares, A., Suzuki, M., Yai, T. (2016). Passenger Satisfaction and Mental Adaptation under Adverse Conditions: Case Study in Manila. Journal of Public Transportation, 19 (4), 144–160. doi: https://doi.org/10.5038/2375-0901.19.4.9
Chi, C.-F., Lin, S.-Z. (2018). Classification scheme and prevention measures for caught-in-between occupational fatalities. Applied Ergonomics, 68, 338–348. doi: https://doi.org/10.1016/j.apergo.2017.12.007
Xu, X., Liu, J., Li, H., Hu, J.-Q. (2014). Analysis of subway station capacity with the use of queueing theory. Transportation Research Part C: Emerging Technologies, 38, 28–43. doi: https://doi.org/10.1016/j.trc.2013.10.010
Walker, G., Strathie, A. (2016). Big data and ergonomics methods: A new paradigm for tackling strategic transport safety risks. Applied Ergonomics, 53, 298–311. doi: https://doi.org/10.1016/j.apergo.2015.09.008
Sugiono, S., Nurlaela, S., Kusuma, A., Wicaksono, A., Lukodono, R. P. (2020). Investigating the Noise Barrier Impact on Aerodynamics Noise: Case Study at Jakarta MRT. Advances in Intelligent Systems and Computing, 189–197. doi: https://doi.org/10.1007/978-981-15-4409-5_17
Suyotno, S. (1985). Meningkatkan Produktivitas dengan Ergonomi. Jakarta: Pertja.
Hartono, M. (2018). Indonesian anthropometry update for special populations incorporating Drillis and Contini revisited. International Journal of Industrial Ergonomics, 64, 89–101. doi: https://doi.org/10.1016/j.ergon.2018.01.004
Igarashi, Y., Kubota, S., Takemoto, M., Kishimoto, K., Inoguchi, K., Yamamoto, Y. et. al. (2012). Investigation on Viewing Angle Requirements and Glare with Respect to Size of Flat-panel Television Displays. SID Symposium Digest of Technical Papers, 43 (1), 820–823. doi: https://doi.org/10.1002/j.2168-0159.2012.tb05911.x
Faulkner, L. (2003). Beyond the five-user assumption: Benefits of increased sample sizes in usability testing. Behavior Research Methods, Instruments, & Computers, 35 (3), 379–383. doi: https://doi.org/10.3758/bf03195514
Lu, M., Wang, C., Lanir, J., Zhao, N., Pfister, H., Cohen-Or, D., Huang, H. (2020). Exploring Visual Information Flows in Infographics. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. doi: https://doi.org/10.1145/3313831.3376263
Bigelow, C. (2019). Typeface features and legibility research. Vision Research, 165, 162–172. doi: https://doi.org/10.1016/j.visres.2019.05.003
Bouchnita, A., Jebrane, A. (2020). A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions. Chaos, Solitons & Fractals, 138, 109941. doi: https://doi.org/10.1016/j.chaos.2020.109941
Badrinath, S., Balakrishnan, H., Clemons, E., Reynolds, T. (2018). Evaluating the Impact of Uncertainty on Airport Surface Operations. 2018 Aviation Technology, Integration, and Operations Conference. doi: https://doi.org/10.2514/6.2018-4242
Clark, R., Freedberg, M., Hazeltine, E., Voss, M. W. (2015). Are There Age-Related Differences in the Ability to Learn Configural Responses? PLOS ONE, 10 (8), e0137260. doi: https://doi.org/10.1371/journal.pone.0137260
Lee, A., Archer, J., Wong, C. K. Y., Chen, S.-H. A., Qiu, A. (2013). Age-Related Decline in Associative Learning in Healthy Chinese Adults. PLoS ONE, 8 (11), e80648. doi: https://doi.org/10.1371/journal.pone.0080648
Parks, L., Guay, R. P. (2009). Personality, values, and motivation. Personality and Individual Differences, 47 (7), 675–684. doi: https://doi.org/10.1016/j.paid.2009.06.002
Odoemene, O., Pisupati, S., Nguyen, H., Churchland, A. K. (2018). Visual Evidence Accumulation Guides Decision-Making in Unrestrained Mice. The Journal of Neuroscience, 38 (47), 10143–10155. doi: https://doi.org/10.1523/jneurosci.3478-17.2018
Lorteije, J. A. M., Zylberberg, A., Ouellette, B. G., De Zeeuw, C. I., Sigman, M., Roelfsema, P. R. (2015). The Formation of Hierarchical Decisions in the Visual Cortex. Neuron, 87 (6), 1344–1356. doi: https://doi.org/10.1016/j.neuron.2015.08.015
Pušnik, N., Podlesek, A., Možina, K. (2016). Typeface comparison – Does the x-height of lower-case letters increased to the size of upper-case letters speed up recognition? International Journal of Industrial Ergonomics, 54, 164–169. doi: https://doi.org/10.1016/j.ergon.2016.06.002
Singhal, A., Monaco, S., Kaufman, L. D., Culham, J. C. (2013). Human fMRI Reveals That Delayed Action Re-Recruits Visual Perception. PLoS ONE, 8 (9), e73629. doi: https://doi.org/10.1371/journal.pone.0073629
Copyright (c) 2021 Sugiono Sugiono, Willy Satrio N, Teuku Anggara, Siti Nurlaela, Andyka Kusuma, Achmad Wicaksono, Rio P. Lukodono
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