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Estimating the safety performance function for urban unsignalized four-legged one-way intersections in Palermo, Italy

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Starting from consideration that urban intersections are sites with promise for safety and operational improvements, the paper describes the steps taken to develop a crash predictive model for estimating the safety performance of urban unsignalized intersections located in Palermo, Italy. The focus is on unsignalized four-legged one-way intersections widespread in Italian downtowns. The sample considered in the study consist of 92 intersections in Palermo, Italy. For the study were collected crashes occurred in the sites during the years 2006-2012, geometric design and functional characteristics and traffic flow. Results showed that data were overdispersed and NB1 distributed. In order to account for the correlation within responses Generalized Estimating Equations (GEE) were used under different working correlation matrices.
Rocznik
Strony
41--54
Opis fizyczny
Bibliogr. 27 poz., il., tab.
Twórcy
autor
  • Department of Civil, Environmental, Aerospace, Materials Engineering, Università di Palermo, Italy
autor
  • Department of Civil, Environmental, Aerospace, Materials Engineering, Università di Palermo, Italy
autor
  • Faculty of Engineering and Architecture, Università Kore di Enna, Italy
autor
  • Department of Energy, Information Engineering and Mathematical Models, Università di Palermo, Italy
autor
  • Department of Civil, Environmental, Aerospace, Materials Engineering, Università di Palermo, Italy
Bibliografia
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  • 3. M. POCH, F. MANNERING, Negative Binomial Analysis of intersection-accident frequencies, ASCE Journal of Transportation Engineering, 122, 2, 105-113, 1996.
  • 4. D. LORD, B. N. PERSAUD, Accident Prediction Models With and Without Trend: Application of the Generalized Estimating Equations Procedure, Transportation Research Record, 1717, 102-108, 2000.
  • 5. J. OH, C. LYON, S. P. WASHINGTON, B. N. PERSAUD, J. BARED, Validation of the FHWA Crash Models for Rural Intersections: Lessons Learned, Transportation Research Record, 1840, 1, 41-49, 2003.
  • 6. M. ABDEL-ATY, H. SALKAPURAM, C. LEE, AND P. A. BRADY, A Simplistic, Practical Approach To Identify Traffic Crash Profiles at Signalized Intersections, ITE Journal, 76, 4, 33-28, 2006.
  • 7. AASHTO, Highway Safety Manual, 1st Edition, ISBN 978-1-56051-477-0, Washington DC, 2010.
  • 8. L. RODEGERDTS, M. BLOGG, E. WEMPLE, E. MYERS, M. KYTE, M. DIXON, G. LIST, A. FLANNERY, R. TROUTBECK, W. BRILON, N. WU, B. N. PERSAUD, C. LYON, D. HARKEY, Application of roundabouts in the US, NCHRP Report 572: Roundabouts in the US, TRB, Washington, DC, 2007.
  • 9. S. TURNER, B. N. PERSAUD, M. CHOU, C. LYON, A. Roozenburg, International crash experience comparisons using prediction models, Proc. of the 2007 Annual TRB Meeting, Washington, D.C., USA, 2007.
  • 10. S. D. CAFISO, C. D’AGOSTINO, Safety performance function for motorways using generalized estimation, Procedia-Social and Behavioral Sciences, 53, 901-910, 2012.
  • 11. S. VIEIRA GOMES, S. R. GEEDIPALLY, D. LORD, Estimating the safety performance of urban intersections in Lisbon, Portugal, Safety Science, 50, 9, 1732-1739, 2012.
  • 12. Automobile Club of Italy, Car trend 2000-2012, Statistic area, studies & search direction, Management control office, Automobile Club of Italy & National Institute of Statistics, Rome, Italy, 2000-2012.
  • 13. O. GIUFFRÈ, A. GRANÀ, T. GIUFFRÈ, R. MARINO, Improving reliability of road safety estimates based on high correlated accident counts, Transportation Research Record, 2019, 197-204, 2007.
  • 14. O. GIUFFRÈ, A. GRANÀ, F. CORRIERE, R. MARINO, Handling under-dispersion in calibrating Safety Performance Function at urban, four-leg, signalized intersections, Journal of Transportation Safety & Security, 3, 3, 174-188, 2011.
  • 15. O. GIUFFRÈ, A. GRANÀ, T. GIUFFRÈ, R. MARINO, Accounting for Dispersion and Correlation in Estimating Safety Performance Functions. An Overview Starting from a Case Study, Modern Applied Science, 7, 2, 11-23, 2013.
  • 16. K. M. BAUER, D. W. HARWOOD, Statistical models of at-grade intersection accidents, addendum (FHWARD-99-094), U.S. Department of Transportation, Federal Highway Administration Washington, DC, 2000.
  • 17. H. MCGEE, S. TAORI, B. N. PERSAUD, Crash experience warrant for traffic signals (NCHRP Report 491): Transportation Research Board, National Cooperative Highway Research Program Washington, DC, 2003.
  • 18. M. J. MAHER & I. SUMMERSGILL, A comprehensive methodology for the fitting of predictive accident models, Accident Analysis & Prevention, 28, 3, 281-296, 1996.
  • 19. P. MCCULLAGH, J. A. NELDER, Generalized linear models, Chapman and Hall: London and New York, 1989.
  • 20. D. LORD, F. MANNERING, The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives, Transportation Research Part A: Policy and Practice, 44, 5, 291-305, 2010.
  • 21. S. MIAOU, The relationship between truck accidents and geometric design of road sections: Poisson versus negative Binomial regressions, Accident Analysis and Prevention, 26, 4, 471-482, 1994.
  • 22. CAMERON, A. C., & TRIVEDI, P. K., Regression Analysis of Count Data, Cambridge University Press; Cambridge, 1998.
  • 23. K. Y. LIANG, S. L. ZEGER, Longitudinal data analysis using generalized linear models, Biometrika, 73, 1, 13–22, 1986.
  • 24. P. J. DIGGLE, P. HEAGERTY, K. Y. LIANG, S. L. ZEGER, Analysis of Longitudinal Data, New York Oxford University Press, 396 p. ISBN: 0198524846, 2002.
  • 25. H. AKAIKE, A new look at the statistical model identification, IEEE Transactions on Automat Control, 19(6): 716 – 723, DOI: 10.1109/TAC.1974.1100705, 1974.
  • 26. W. PAN, Akaike’s information criterion in generalized estimating equations, Biometrics 57, 120-125, 2001.
  • 27. D. LORD, Modelling motor vehicle crashes using Poisson-Gamma models: Examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter, Accident Analysis & Prevention 38, 4, 751-766, 2006.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c9caa920-5a2b-412d-8b39-69cd00d8f01d
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