Abstract
Specialists of different fields (from traffic management and control to logistics) are interested in the market of autonomous cars development. This could give a synergistic effect, because it can improve reliability, safety, efficiency and sustainability of the transport system. However, intellectualization of the transport system requires solving problems that arise (here, under the intellectualization we understand the process of transforming the transport system into the Intelligent Transport System). We propose the classification of risks that will arise while transport system’s intellectualization. Classifier is made in accordance with risk characteristics that will allow directing efforts to prevent the most probable risks, as well as to reduce their severity in case of occurrence. We also suggest software based on a multiple-factor analysis of information that identifies the causes of critical situations. We have modified Haddon matrix, which allows determining factors that affect the number of accidents and the severity of their consequences, as well as the measures most effectively contributing to improving road safety. If recommendations’ implementation caused increase of road safety, the proposed software enters these scripts into the knowledge base. This has allowed us to conclude what actions have helped to increase road safety in the city and what have to be corrected. If recommended decisions haven’t had an expected positive effect, Haddon matrix was revised and adjusted in accordance with the actual results.
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References
Pernebekov, S.S., et al.: Modeling of traffic flows with due regard to ecological criteria. Life Sci. J. 11(5), 300–302 (2014)
Zhang, Y., et al.: New systems-based method to conduct analysis of road traffic accidents. Transp. Res. Part F: Traffic Psychol. Behav. 54, 96–109 (2018)
Hedlund, J., et al.: Pre-offense alcohol intake in homicide offenders and victims: a forensic-toxicological case-control study. J. Forensic Leg. Med. 56, 55–58 (2018)
Ramana, G.V., Reddy, N.S., Praneeth, D.M.V.: Analysis on traffic safety and road accidents: a case study on selected roads in Muscat, Oman. Int. J. Civ. Eng. Technol. 9(2), 417–427 (2018)
M’bailara, K., et al.: Emotional reactivity: beware its involvement in traffic accidents. Psychiatry Res. 262, 290–294 (2018)
Hicks, D.J., et al.: The safety of autonomous vehicles: lessons from philosophy of science. IEEE Technol. Soc. Mag. 37(1), 62–69 (2018)
Xie, G., et al.: Situational assessments based on uncertainty-risk awareness in complex traffic scenarios. Sustain. (Swit.) 9(9), 1582 (2017)
IEC/ISO 31010: Risk management – risk assessment techniques (2009). https://www.iso.org/standard/51073.html. Accessed 14 Jan 2019
Agarwal, P.K.: A methodology for ranking road safety hazardous locations using analytical hierarchy process. Proc. – Soc. Behav. Sci. 104, 1030–1037 (2013)
Development of Risk Models for the Road Assessment Programme, 46: http://www.eurorap.org/wp-content/uploads/2015/04/20120308-website-sdl-DAL-TRL_RAP_Model_Development_Final_3.pdf. Accessed 14 Jan 2019
Vulnerability and Risk Analysis of Road Infrastructure in Reykjavik, 41: http://www.vegagerdin.is/vefur2.nsf/Files/Vulnerability_riskanalysis-PrelimReport/$file/Vulnerability_risk%20analysis-PrelimReport.pdf. Accessed 14 Jan 2019
Beinarovica, A., Gorobetz, M., Levchenkov, A.: Innovative neuro-fuzzy system of smart transport infrastructure for road traffic safety. IOP Conf. Ser.: Mater. Sci. Eng. 236(1), 012095 (2017)
Shah, S.A.R., et al.: Road safety risk assessment: an analysis of transport policy and management for low- middle- and high-income Asian countries. Sustain. (Swit.) 10(2), 389 (2018)
Makarova, I., Shubenkova, K., Gabsalikhova, L.: Analysis of the city transport system’s development strategy design principles with account of risks and specific features of spatial development. Transp. Probl. 12(1), 125–138 (2017)
Haddon Jr., W.: Advances in the epidemiology of injuries as a basis for public policy. Public Health Rep. 95, 411–421 (1980)
Makarova, et al.: Application of data mining technology to optimize the city transport network. IEEE AICT 2016, Baku, Azerbaijan, paper #7991674 (2016)
Behnood, H.R., et al.: A fuzzy decision-support system in road safety planning. Proc. Inst. Civ. Eng.: Transp. 170(5), 305–317 (2017)
Ryder, B., et al.: Preventing traffic accidents with in-vehicle decision support systems - the impact of accident hotspot warnings on driver behavior. DSS 99, 64–74 (2017)
Software Source Code: https://github.com/timurbakibayev/ta. Accessed 14 Jan 2019
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Research is partially funded by national grant No. BR05236644.
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Makarova, I., Shubenkova, K., Bakibayev, T., Pashkevich, A. (2020). The Concept of the Software to Analyse Road Safety Statistics and Support Decision Making Process. In: Varhelyi, A., Žuraulis, V., Prentkovskis, O. (eds) Vision Zero for Sustainable Road Safety in Baltic Sea Region. VISZERO 2018. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-22375-5_6
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DOI: https://doi.org/10.1007/978-3-030-22375-5_6
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