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Modelling the ranking of lithuanian railways level crossing by safety level

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article describes the assessment of safety of Lithuanian Railways level crossing. The statistical analysis of the railway accidents in Lithuania and abroad in recent years has shown that about 30% of all transport accidents in railway occur at railway level crossings. The safety assessment of the country's crossings is carried out considering the following technical criteria: the category of crossing, visibility, the intensity of the movement of trains and road vehicle, the width of the railway crossing, and the maximum speed of trains. Applying the binary model of logistic regression, the probability of accidents at the 337 railway crossings of the country was calculated. Depending on the degree of risk or the probability of accident, the country's railway crossings are ranked. The most dangerous crossings of four regions in the country were identified. Finally, the main conclusions and recommendations are presented.
Czasopismo
Rocznik
Strony
11--22
Opis fizyczny
Bibliogr. 15 poz.
Twórcy
autor
  • Vilnius Gediminas Technical University Department of Railway Transport J. Basanavičiaus g. 28, LT-03224 Vilnius, Lithuania
autor
  • Vilnius Gediminas Technical University Department of Railway Transport J. Basanavičiaus g. 28, LT-03224 Vilnius, Lithuania
  • JSC “Lithuanian Railways” Mindaugo g 12, LT-03603 Vilnius, Lithuania
Bibliografia
  • 1. Bonvicini, S. & Leonelli, P. & Spadoni, G. Risk analysis of hazardous materials transportation: evaluating uncertainty by means of fuzzy logic. Journal of Hazardous Materials. 1998. Vol. 62(1). P. 59-74.
  • 2. Bureika, G. & Bekintis, G. & Liudvinavičius, L. & Vaičiūnas, G. Applying analytic hierarchy process to assess traffic safety risk of railway infrastructure. Eksploatacja i Niezawodnosc –Maintenance and Reliability. 2013. Vol. 15(4). P. 376-383.
  • 3. Hu, S-R. & Li, C-S. & Lee, C-K. Investigation of key factors for accident severity at railroad grade crossings by using a logit model. Safety Science. 2010. Vol. 48(2). P. 186-194.
  • 4. Ishak, A.Z. & Yue, W.L. & Somenahalli, S. Level Crossing Modelling Using Petri Nets Approach and Π–Tool. Asian Transport Studies. 2010. Vol. 1(2). P. 107-121.
  • 5. Lerner, N.D. & Llaneras, R.E. & McGree, H.W. & Stephens, D.E. Traffic Control Devices for Passive Railroad-Highway Grade Crossings. National Cooperative Highway Research Program, Report 470. Washington, DC: Transportation Research Board. 2002.
  • 6. Liu, R. & Huang, Z & Hao, W. Potential Issues and Challenges in Implementing the Railroad Safety Improvement Act of 2008 Based on a National Survey. Journal of Transportation Safety & Security. 2011. Vol. 3(4). P. 252-271. doi: 10.1080/19439962.2011.607937
  • 7. Lobb, B. Trespassing in the tracks: a review of railway pedestrian safety research. Journal of Safety Research. 2006. Vol. 37(4). P. 359-365.
  • 8. Mok, S.C. & Savage, I. Why Has Safety Improved at Rail-Highway Grade Crossings? Risk Analysis. 2005. Vol. 25 (4). P. 867-881.
  • 9. Oh, J. & Washington, S.P. & Nam, D. Accident prediction model for railway-highway interfaces. Accident Analysis & Prevention. 2006. Vol. 38(2). P. 346-356.
  • 10. Review of Network Rail`s All Level Crossing Risk Model (ALCRM). Available at:http://webarchive.nationalarchives.gov.uk/20131001175041/http:/www.railreg. gov.uk/upload/pdf/sres-ALCRM_rev.pdf
  • 11. Saccomanno, F.F. & Fu, L. & Miranda-Moreno, L.F. Risk-Based Model for Identifying Highway-Rail Grade Crossing Blackspots. Journal of the Transportation Research Board. 2004. P. 127-135.
  • 12. Tey, L.S. & Ferreira, L. & Wallace, A. Measuring driver responses at railway level crossings. Accident Analysis and Prevention. 2011. Vol. 43. P. 2134-2141.
  • 13. Wullems, C. Towards the adoption of low-cost rail level crossing warning devices in regional areas of Australia: A review of current technologies and reliability issues. Safety Science. 2011. Vol. 49(8-9). P. 1059-1073.
  • 14. Yan, X. & Richards, S. & Sub, X. Using hierarchical tree-based regression model to predict train–vehicle crashes at passive highway-rail grade crossings. Accident Analysis & Prevention. 2010. Vol. 42(1). P. 64-74.
  • 15. Ye, X. & Pendyala, R.M. & Shankar, V. & Konduri, K.C. A simultaneous equations model of crash frequency by severity level for freeway sections. Accident Analysis and Prevention. 2013. Vol. 57. P. 140-149.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c6f4eab1-3610-44df-9ea6-93f5ceddff0d
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