Algorithm of automated annotation of areas of roads with increased accidents

Michail Eliseev; Alexandr Blinov; Alexandr Lipenkov

doi:10.1051/matecconf/202133401007

All issues

Volume 334 (2021)

MATEC Web Conf., 334 (2021) 01007

References

Open Access

Issue		MATEC Web Conf. Volume 334, 2021 The VI International Scientific and Practical Conference “Information Technologies and Management of Transport Systems” (ITMTS 2020)


Article Number		01007
Number of page(s)		7
Section		Intelligent Transport Systems
DOI		https://doi.org/10.1051/matecconf/202133401007
Published online		15 January 2021

K. Duivenvoorden, The relationship between traffic volume and road safety on the secondary road network, A literature review. D-2010-2.SWOV Institute for Road Safety Research, Leidschendam (2010) [Google Scholar]
T.F. Golob, W. Recker, & Y. Pavlis, Probabilistic models of freeway safety performance using traffic flow data as predictors. In: Safety Science, 46(9), p. 1306-1333 (2008) [Google Scholar]
L.Y. Chang, W.C. Chen, Data mining of tree-based models to analyze freeway accident frequency, Journal of Safety Research, 36, p. 365-375 (2005). [Google Scholar]
T. Brijs, D. Karlis, G. Wets, Studying the effect of weather conditions on daily crash counts using a discrete time series model, Accident Analysis and Prevention, 40(3), 1180-1190 (2008) [Google Scholar]
Michael Eliseev, Tatyana Tomchinskaya, Alexandr Lipenkov, Alexandr Blinov, Using 3D-modeling Technologies to Increase Road Safety, Transportation Research Procedia, Volume 20, Pages 1-756 (2017) [Google Scholar]
Daito Kodama, and Ozawa, Real Time Accident Risk Information Provision on a Urban Expressway Network: Prediction Model Analysis and Development of a Provision System, Proceedings of the 51st Spring Conference of the Committee of Infrastructure Planning and Management, (2015) [Google Scholar]
B.H. DeLucia, and R.A. Scopatz, E-Crash: The Model Electronic Crash Data Collection System, Report DOT HS 811 326, National Highway Traffic Safety Administration (NHTSA), Washington, DC. (2010) [Google Scholar]
A. Graettinger, J.K. Lindly, and G.J. Mistry, Display and Analysis of Crash Data. Report 03102, University Transportation Center for Alabama (UTCA), Tuscaloosa, AL. (2005) [Google Scholar]
G. Khan, K.R. Santiago-Chaparro, X. Qin, and D.A. Noyce, Application and Integration of Lattice Data Analysis, Network K-Functions, and Geographic Information System Software to Study Ice-Related Crashes, Transportation Research Record: Journal of the Transportation Research Board, 2136, pp. 67-76. (2009) [Google Scholar]
Alexander Paz, Naveen Veeramisti, Romesh Khaddar, Hanns de la Fuente-Mella, and Luiza Modorcea, Traffic and Driving Simulator Based on Architecture of Interactive Motion, The Scientific World Journal, 2015, Article ID 340576, 9 pages, (2015). [Google Scholar]
S. Espie, D. Gattuso, F. Galante, A hybrid traffic model coupling macro and behavioural micro simulation, Proceedings of the 85th Annual Meeting Transportation Research Board; Washington, DC, USA (2006) [Google Scholar]
P. Songchitruksa, K.N. Balke, Assessing weather, environment, and loop data for real-time freeway incident prediction, Transport Research Record 1959, pp. 105-113. (2006) [Google Scholar]
M. Kilpeläinen, & H. Summala, Effects of weather and weather forecasts on driver behaviour, Transportation Research Part F 10, pp. 288-299 (2007) [Google Scholar]
K. Keay, I. Simmonds, Road accidents and rainfall in a large Australian city, Accident Analysis and Prevention, 38(3), pp. 445-454 (2006) [Google Scholar]
L. Fridström, J. Ifver, S. Ingebrigtsen, R. Kulmala, L.K. Thomsen, Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts, Accident Analysis and Prevention, 27(1), pp. 1-20 (1995) [Google Scholar]
M.E. Eliseev, N.A. Kuzmin, A.A. Repnikov, Analyzing weather factors of an interactive map for accident subsystem, Intellekt. Innovacii. Investicii., 2, pp. 124-127 (2016) [Google Scholar]

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[1] K. Duivenvoorden, The relationship between traffic volume and road safety on the secondary road network, A literature review. D-2010-2.SWOV Institute for Road Safety Research, Leidschendam (2010) [Google Scholar]

[2] T.F. Golob, W. Recker, & Y. Pavlis, Probabilistic models of freeway safety performance using traffic flow data as predictors. In: Safety Science, 46(9), p. 1306-1333 (2008) [Google Scholar]

[3] L.Y. Chang, W.C. Chen, Data mining of tree-based models to analyze freeway accident frequency, Journal of Safety Research, 36, p. 365-375 (2005). [Google Scholar]

[4] T. Brijs, D. Karlis, G. Wets, Studying the effect of weather conditions on daily crash counts using a discrete time series model, Accident Analysis and Prevention, 40(3), 1180-1190 (2008) [Google Scholar]

[5] Michael Eliseev, Tatyana Tomchinskaya, Alexandr Lipenkov, Alexandr Blinov, Using 3D-modeling Technologies to Increase Road Safety, Transportation Research Procedia, Volume 20, Pages 1-756 (2017) [Google Scholar]

[6] Daito Kodama, and Ozawa, Real Time Accident Risk Information Provision on a Urban Expressway Network: Prediction Model Analysis and Development of a Provision System, Proceedings of the 51st Spring Conference of the Committee of Infrastructure Planning and Management, (2015) [Google Scholar]

[7] B.H. DeLucia, and R.A. Scopatz, E-Crash: The Model Electronic Crash Data Collection System, Report DOT HS 811 326, National Highway Traffic Safety Administration (NHTSA), Washington, DC. (2010) [Google Scholar]

[8] A. Graettinger, J.K. Lindly, and G.J. Mistry, Display and Analysis of Crash Data. Report 03102, University Transportation Center for Alabama (UTCA), Tuscaloosa, AL. (2005) [Google Scholar]

[9] G. Khan, K.R. Santiago-Chaparro, X. Qin, and D.A. Noyce, Application and Integration of Lattice Data Analysis, Network K-Functions, and Geographic Information System Software to Study Ice-Related Crashes, Transportation Research Record: Journal of the Transportation Research Board, 2136, pp. 67-76. (2009) [Google Scholar]

[10] Alexander Paz, Naveen Veeramisti, Romesh Khaddar, Hanns de la Fuente-Mella, and Luiza Modorcea, Traffic and Driving Simulator Based on Architecture of Interactive Motion, The Scientific World Journal, 2015, Article ID 340576, 9 pages, (2015). [Google Scholar]

[11] S. Espie, D. Gattuso, F. Galante, A hybrid traffic model coupling macro and behavioural micro simulation, Proceedings of the 85th Annual Meeting Transportation Research Board; Washington, DC, USA (2006) [Google Scholar]

[12] P. Songchitruksa, K.N. Balke, Assessing weather, environment, and loop data for real-time freeway incident prediction, Transport Research Record 1959, pp. 105-113. (2006) [Google Scholar]

[13] M. Kilpeläinen, & H. Summala, Effects of weather and weather forecasts on driver behaviour, Transportation Research Part F 10, pp. 288-299 (2007) [Google Scholar]

[14] K. Keay, I. Simmonds, Road accidents and rainfall in a large Australian city, Accident Analysis and Prevention, 38(3), pp. 445-454 (2006) [Google Scholar]

[15] L. Fridström, J. Ifver, S. Ingebrigtsen, R. Kulmala, L.K. Thomsen, Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts, Accident Analysis and Prevention, 27(1), pp. 1-20 (1995) [Google Scholar]

[16] M.E. Eliseev, N.A. Kuzmin, A.A. Repnikov, Analyzing weather factors of an interactive map for accident subsystem, Intellekt. Innovacii. Investicii., 2, pp. 124-127 (2016) [Google Scholar]