APPLICATION OF TECHNOLOGIES OF HIGH PERFORMANCE COMPUTING IN RESEARCH OF ROAD TRAFFIC SAFETY
DOI:
https://doi.org/10.24867/16BE18VunicKeywords:
Road traffic accidents, High performance computing, Machine learning, R, Apache SparkAbstract
The paper presents how technologies of high performance computing are applied in an analysis of factors affecting the severity level of road traffic accidents. The paper specifies the employed machine learning technologies and algorithms, used data sets and applied methodology with presented results of the research. The solutions are implemented using R programming language and Apache Spark engine for distributed data processing.
References
[1] World Health Organization, “Road traffic injuries”, World Health Organization (WHO), Jun 2021. Dostupno na adresi: https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries (pristupljeno u avgustu 2021.)
[2] S. Moosavi, M. H. Samavatian, S. Parthasarathy, R. Teodorescu & R. Ramnath, “Accident risk prediction based on heterogeneous sparse data: New dataset and insights”, Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019.
[3] S. Moosavi, M. H. Samavatian, S. Parthasarathy & R. Ramnath, “A Countrywide Traffic Accident Dataset”, arXiv preprint arXiv:1906.05409, 2019.
[4] C. Parra, C. Ponce & S. F. Rodrigo, “Evaluating the Performance of Explainable Machine Learning Models in Traffic Accidents Prediction in California”, 2020 39th International Conference of the Chilean Computer Science Society (SCCC), pp. 1-8, doi: 10.1109/SCCC51225.2020.9281196, 2020.
[5] “Подаци о саобраћајним незгодама по ПОЛИЦИЈСКИМ УПРАВАМА и ОПШТИНАМА (СН) - Отворени подаци”, Data.gov.rs, 2021. Dostupno na adresi: https://data.gov.rs/sr/datasets/podatsi-o-saobratshajnim-nezgodama-po-politsijskim-upravama-i-opshtinama/ (pristupljeno u avgustu 2021)
[6] S. Shalev-Shwartz & S. Ben-David, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge: Cambridge University Press. doi:10.1017/CBO9781107298019, 2014.
[7] N. Lunardon, G. Menardi & N. Torelli, “ROSE: a Package for Binary Imbalanced Learning”, The R Journal, vol. 6, no. 1, p. 79, 2014. Available: 10.32614/rj-2014-008.
[8] A. Zheng, “Evaluating Machine Learning Models”, O'Reilly Media, Inc., Sep. 2015.
[2] S. Moosavi, M. H. Samavatian, S. Parthasarathy, R. Teodorescu & R. Ramnath, “Accident risk prediction based on heterogeneous sparse data: New dataset and insights”, Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019.
[3] S. Moosavi, M. H. Samavatian, S. Parthasarathy & R. Ramnath, “A Countrywide Traffic Accident Dataset”, arXiv preprint arXiv:1906.05409, 2019.
[4] C. Parra, C. Ponce & S. F. Rodrigo, “Evaluating the Performance of Explainable Machine Learning Models in Traffic Accidents Prediction in California”, 2020 39th International Conference of the Chilean Computer Science Society (SCCC), pp. 1-8, doi: 10.1109/SCCC51225.2020.9281196, 2020.
[5] “Подаци о саобраћајним незгодама по ПОЛИЦИЈСКИМ УПРАВАМА и ОПШТИНАМА (СН) - Отворени подаци”, Data.gov.rs, 2021. Dostupno na adresi: https://data.gov.rs/sr/datasets/podatsi-o-saobratshajnim-nezgodama-po-politsijskim-upravama-i-opshtinama/ (pristupljeno u avgustu 2021)
[6] S. Shalev-Shwartz & S. Ben-David, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge: Cambridge University Press. doi:10.1017/CBO9781107298019, 2014.
[7] N. Lunardon, G. Menardi & N. Torelli, “ROSE: a Package for Binary Imbalanced Learning”, The R Journal, vol. 6, no. 1, p. 79, 2014. Available: 10.32614/rj-2014-008.
[8] A. Zheng, “Evaluating Machine Learning Models”, O'Reilly Media, Inc., Sep. 2015.
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Published
2022-01-31
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Section
Electrotechnical and Computer Engineering
