Study of the influence of road congestion on the fatigue level of a sanguine driver
DOI:
https://doi.org/10.15587/2312-8372.2019.191275Keywords:
traffic congestion, fatigue level, temperament, sanguine driver, road safetyAbstract
The object of research is the process of the driver’s labor activity on city roads in the city’s transport system during the transportation of goods and passengers. The influence of traffic congestion on the functional state of a sanguine driver is studied, which is one of the most common types of temperament. The main hypothesis of the study is that the level of driver fatigue in traffic congestion, which affects the driver’s reaction time and road safety, depends on the driver’s condition and traffic congestion parameters. The driver’s fatigue level is determined based on the concept of the cardiovascular system, as an indicator of the adaptive and adaptive activity of the body by measuring the electrocardiogram. In this case, the unevenness of the cardio intervals is analyzed, which is a universal response to any kind of load. Fatigue level is calculated in arbitrary units according to a special algorithm that takes into account statistical indicators, histogram indicators and data of spectral analysis of cardio intervals. Using a non-linear model of changing the functional state of a sanguine driver, the patterns of changes in the fatigue level under various conditions of stay in traffic congestion are obtained. It is revealed that the most significant factor that affects the final level of driver fatigue in traffic congestion is its initial value before traffic congestion. The second most important parameter affecting the change in the fatigue level of the sanguine driver is the duration of traffic congestion, which affects the initial function only together with the initial fatigue level. The influence of the age of the sanguine driver on the fatigue level in the traffic congestion is manifested to a lesser extent. However, the conditions of stay in traffic congestion most noticeably affect older drivers (sixty or more years) compared with young drivers of twenty years. Analysis of the research results shows that traffic congestions lasting more than twelve minutes lead to a significant increase in the fatigue level of a sanguine driver. This may increase the likelihood of a traffic accident. The trends in the fatigue level of sanguine driver in traffic congestions identified during the study allow to predict the driver’s behavior after exiting a traffic congestion and evaluate possible patterns of road traffic development that directly affect road safety.
References
- Lobanov, E. M. (1980). Proektirovanie dorog i organizaciia dvizheniia s uchetom psikhofiziologii voditelia. Moscow: Transport, 311.
- Hiuliev, N. U. (2016). Liudskyi faktor i dorozhni zatory. Kharkiv: KhNUMH im. O. M. Beketova, 235.
- Kundelekov, A. G. (2012). Vliianie transportnykh zatorov na psikhovegetativnii status voditelei obschestvennogo transporta s uchetom vozrasta i stazha raboty. Fundamentalnye issledovaniia, 12 (1), 82–85.
- Zhang, L., Jia, Y., Niu, Z., Liao, C. (2014). Widespread Traffic Congestion Prediction for Urban Road Network Based on Synergetic Theory. Journal of Systems Science and Information, 2 (4), 366–371. doi: http://doi.org/10.1515/jssi-2014-0366
- Yang, S. (2013). On feature selection for traffic congestion prediction. Transportation Research Part C: Emerging Technologies, 26, 160–169. doi: http://doi.org/10.1016/j.trc.2012.08.005
- Qi, W., Pei, Y., Song, M., Bie, Y. (2013). Pattern Analysis of Driver’s “Pressure-State-Response” in Traffic Congestion. Discrete Dynamics in Nature and Society, 6, 1–11. doi: http://doi.org/10.1155/2013/853845
- Son, S., Baek, Y. (2015). Design and Implementation of Real-Time Vehicular Camera for Driver Assistance and Traffic Congestion Estimation. Sensors, 15 (8), 20204–20231. doi: http://doi.org/10.3390/s150820204
- Drum, D. K. (2014). Counteracting traffic congestion using intelligent driver feedback. doi: http://doi.org/10.32469/10355/44263
- Lizbetin, J., Bartuska, L. (2017). The Influence of Human Factor on Congestion Formation on Urban Roads. Procedia Engineering, 187, 206–211. doi: http://doi.org/10.1016/j.proeng.2017.04.366
- Ito, T., Kaneyasu, R. (2017). Predicting traffic congestion using driver behavior. Procedia Computer Science, 112, 1288–1297. doi: http://doi.org/10.1016/j.procs.2017.08.090
- Baevskii, P. M. (1979). Prognozirovanie sostoianii na grani normy i patologii. Moscow: Medicina, 298.
- Giulev, N. U., Dolia, V. K. (2012). Nonlinear model of changes in functional state in driver sangvinnika traffic congestion. Eastern-European Journal of Enterprise Technologies, 3 (4 (57)), 17–19. Available at: http://journals.uran.ua/eejet/article/view/4008
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Copyright (c) 2020 Nizami Gyulyev, Oleksii Prasolenko, Eugene Litomin, Daria Zinchenko
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