Drivers’ workload measures to verify functionality of ferry boats boarding area

Bosurgi Gaetano; Marra Stellario; Pellegrino Orazio; Massimo Villari

doi:10.5604/01.3001.0014.5506

Authors

Bosurgi Gaetano Department of Engineering, University of Messina Author https://orcid.org/0000-0002-0782-5510
Marra Stellario Department of Engineering, University of Messina Author https://orcid.org/0000-0001-8118-8989
Pellegrino Orazio Department of Engineering, University of Messina Author https://orcid.org/0000-0002-7990-3581
Massimo Villari Department of Scienze matematiche e informatiche, scienze fisiche e scienze della terra, University of Messina Author https://orcid.org/0000-0001-9457-0677

DOI:

https://doi.org/10.5604/01.3001.0014.5506

Keywords:

workload measures, driver behaviour, road safety, driving simulation, complex environment

Abstract

Functionality of a square used for ferry boats boarding has repercussions on safety and comfort of users, as well as on the efficiency of maritime transport. Inadequate use of the infrastructure causes driving errors followed by corrective manoeuvres, loss of time and potential accidents with consequences for community and the maritime transport company. The wide diversification of traffic components and payment methods are generally managed through a traditional horizontal and vertical signage system that does not refer to any current legislation. Therefore, the purpose of this research is to investigate driver's behaviour and the interaction that takes place between the latter and the environmental context. In particular, the authors focused on the study of the driver’s workload in a simulated environment, considering a users' sample and different driving scenarios inside the boarding area, concerning traffic conditions (isolated vehicle or presence of disturbing vehicles) and signs position. All this, in order to evaluate whether any change in a virtual context could bring real benefits to drivers, before being transferred to the real context. The results obtained, in terms of subjective workload and performance measures, have made it possible to judge the different solutions proposed in a simulated environment through synthetic indices referring to the entire boarding place or at certain parts of it. In this way, the manager can decide to change the circulation of the entire square or only some aspects of detail, such as some signals, in the event that they manifest an evident difficulty in the transfer of information. The use of the simulated environment allows greater speed in identifying the best solution, lower costs (avoiding the creation of a critical configuration for circulation) and greater user safety, since risky manoeuvres are identified and corrected by the simulator. The proposed procedure can be used by managers for a correct arrangement of the signs, for the purpose of correctly directing the flows and maximizing the flow rate disposed of.

References

AFANASIEVA, I., GALKIN, A., 2018. Assessing the information flows and established their effects on the results of driver’s activity. Archives of Transport, 45(1), 7-23. https://doi.org/10.5604/01.3001.0012.0938.

ANH SON, L., SUZUKI, T., AOKI, H., 2019. Evaluating driver cognitive distraction by eye tracking: From simulator to driving. Transportation Research Interdisciplinary Perspectives, 4.

BONGIORNO, N., BOSURGI, G., PELLEGRINO, O., SOLLAZZO, G., 2017. How is the Driver’s Workload Influenced by the Road Environment? Procedia Engineering, 187, 5-13.

BOSURGI, G., D’ANDREA, A., PELLEGRINO, O., 2013. What variables affect to a greater extent the driver’s vision while driving?. Transport, 28 (4), 331-340.

BROOKHUIS, K.A., DE WAARD, D., 2010. Monitoring driver’s mental workload in driving simulators using physiological measures. Accident Analysis and Prevention, 42, 898-903.

BUTMEE, T., LANSDOWN, T.C., WALKER, G.H. 2019. Mental workload and performance measurements in driving task: A review literature. Proceedings of the 20th Congress of the International Ergonomics Association: IEA 2018 (Advances in Intelligent Systems and Computing, Springer), 823, 286-294. https://doi.org/10.1007/978-3-319-96074-6_31.

CANTIN, V., LAVALLIÈRE, M., SIMO-NEAU, M., TEASDALE, N., 2009. Mental Workload when driving in a simulator: Effects of age and driving complexity. Accident Analysis and Prevention, 41, 763-771.

CEGARRA, J., CHEVALIER, A., 2008. The use of Tholos software for combining measures of mental workload toward theoretical and methodological improvements. Behav. Res. Methods, 40, 988-1000.

CNOSSEN, F., ROTHENGATTER, T., MEIJMAN, T., 2000. Strategic changes in task performance in simulated car driving as an adaptive response to task demands. Transportation Research Part F, 3, 123-140.

COSTA, N., COSTA, S., PEREIRA, E., AREZES, P.M., 2019. Workload measures recent trends in the driving context. Studies in Systems, Decision and Control, 202, 419-430.

DE WAARD, D., 1991. Driving behavior on a high-accident-rate motorway in the Netherlands. Man in Complex Systems. Eds Weikert, C., Brookhuis, K.A., Ovinius, S. Proceedings of the Europe Chapter of the Human Factors Society Annual Meeting (Work Science Bulletin 7, Work Science Division, Department of Psychology, Lund University, Lund).

DE WAARD, D., 1996. The measurement of drivers’ mental workload. PhD thesis, University of Groningen, Haren, The Netherlands.

DE WAARD, D., BROOKHUIS K.A., HERNANDEZ-GRESS, N., 2001. The feasibility of detecting phone-use related driver distraction. Int. J. Veh. Des., 26, 85–95.

FASTENMEIER, W., 1995. Die verkehrssituation als analyseeinheit im verkehrssystem (The road traffic situation as analysis unit in the road traffic system). Autofahrer und Verkehrssituation en: Neue Wege zur Bewer-tung von Sicherheit und Zuverlässigkeit Moderner Straβenverkehrssysteme, ed. Fastenmeier W. (Köln: Verlag TÜV Rheinland), 27-78.

FASTENMEIER, W., GSTALTER, H., 2007. Driving task analysis as a tool in traffic research and practice. Saf. Sci., 45, 952-979.

HART, S.G., STAVELAND, L.E., 1988. Development of NASA-TLX (task load index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183.

KRAMER, A.F., 1991. Physiological metrics of mental workload: a review of recent progress. Multiple-Task Performance, ed. Damos, D. L. London: Taylor and Francis, 279–328.

KUROSAWA, Y., MOCHIDUKI, HOSHINO, Y., YAMADA, M., 2020. Measurement of Fatigue Based on Changes in Eye Movement during Gaze. IEICE Transactions on Information and Systems. E103.D. 1203-1207. 10.1587/transinf.2019EDL8162.

LEE, Y., WEI, C.-H., CHAO, K.-C., 2017. Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways. Archives of Transport, 43 (3), 91-104.

LI, X., VAEZIPOUR, A., RAKOTONI-RAINY, A., DEMMEL, S., OVIEDO-TRESPALACIOS, O., 2020. Exploring drivers’ mental workload and visual demand while using an in-vehicle HMI for eco-safe driving. Accident Analysis & Prevention, 146.

LIAO, Y., LI., G., LI, S.E., CHENG, B., GREEN, P., 2018. Understanding Driver Response Patterns to Mental Workload Increase in Typical Driving Scenarios. IEEE Access, 6, 35890-35900, doi: 10.1109/ACCESS.2018.2851309.

MARQUART, G., CABRALL, C., DE WINTER, J., 2015. Review of Eye-related Measures of Drivers’ Mental Workload. Procedia Manufacturing 3. 10.1016/j.promfg.2015.07.783.

MUSLIM, N. H. B., SHAFAGHAT, A., KEYVANFAR, A., ISMAIL, M., 2018. Green Driver: driving behaviors revisited on safety. Archives of Transport, 47(3), 49-78. https://doi.org/10.5604/01.3001.0012.6507.

O’DONNELL., R.D., EGGEMEIER, F.T., 1986. Workload assessment methodology. Handbook of Perception and Human Performance, 2, Cognitive Processes and Performance. In Boff, K.R., Kaufman, L., Thomas, J.P., (1st Edition). New York: Wiley.

O’HERN, S., STEPHAN, K., QIU, J., OXLEY, J., 2019. A simulator study of driving behavior and mental workload in mixed-use arterial road environments. Traffic Injury Prevention, 20 (6), 648-654.

PATTEN, C.J.D., KIRCHER, A., ÖSTLUND, J., NILSSON, L., SVENSON, O., 2006. Driver experience and cognitive workload in different traffic environments. Accid. Anal. Prev., 38, 887-894.

PAUBEL, P.-V., 2011. Evaluation d’un Système de Résolution de Conflits, ERASMUS. Apport de l’Oculométrie Comme Mesure de la Charge Mentale Chez les Contrôleurs Aériens En-route. Ph.D. thesis, Université Toulouse le Mirail-Toulouse II, Toulouse.

PAXION, J., GALY, E., BERTHELON, C., 2014. Mental Workload and Driving. Frontiers in Psycohology, 5, 1-11.

PELLEGRINO, O., 2009. An analysis of the effect of roadway design on driver’s workload. Baltic Journal of Road and Bridge Engineering, 4 (2), 45–53.

PELLEGRINO, O., 2012. Prediction of driver’s workload by means of fuzzy techniques. Baltic Journal of Road and Bridge Engineering, 7 (2), 120–128.

REHMANN, A.J., 1995. Handbook of Human Performance Measures and Crew Requirements for Flightdeck Research. DOT/FAA/CTTN95/49.

RUBIO, S., DÍAZ, E., MARTIN, J., PUENTE, J.M., 2004. Evaluation of subjective mental workload: a comparison of SWAT, NASA-TLX, and workload profile methods. Appl. Psychol., 53, 61-86.

STEYVERS, F.J., DE WAARD, D., 2000. Road-edge delineation in rural areas: effects on driving behavior. Ergonomics, 43, 223-238.

WICKENS, C.D., GORDON, S.E., LIU Y. 1998. An Introduction to Human Factors. Engineering., 392-395.