MÉTODO PROBABILÍSTICO PARA IDENTIFICAÇÃO DE ZONAS DE ACUMULAÇÃO DE ACIDENTES

Authors

  • Sara Ferreira Faculdade de Engenharia da Universidade do Porto
  • António Couto Faculdade de Engenharia da Universidade do Porto

DOI:

https://doi.org/10.4237/transportes.v21i3.683

Abstract

Neste trabalho apresenta-se um novo método de definição e identificação de zonas de acumulação (ZAA) de acidentes considerando um modelo de regressão binário. Este modelo permite calcular a probabilidade de um local ser ou não ZAA tendo em conta as características geométricas e funcionais do mesmo. Este novo método foi aplicado ao caso das interseções tendo-se, para tal, gerado uma base de dados fictícia considerando as características da sinistralidade e das interseções da cidade do Porto. Através da simulação de dados é possível conhecer a priori as “verdadeiras” ZAA. O desempenho do método foi analisado com base nos erros resultantes da classificação dos locais em ZAA ou não-ZAA, e comparado com dois dos métodos mais aplicados e analisados – o método de ranking pelo número de acidentes e o método Bayesiano-empírico. Desta análise verificou-se que o método binário proposto tem claramente melhor desempenho.

Downloads

Download data is not yet available.

References

AASHTO (2010) Highway Safety Manual. American Association of State Highway and Transportation Officials, Washington DC.

ISBN: 978-1-56051-477-0

Cafiso, S. e Silvestro, G. D. (2011) Performance of Safety Indicators in Identification of Black Spots on Two-Lane Rural Roads Transportation Research Record: Journal of the Transportation Research Board, 2237, 78-87.

DOI: 10.3141/2237-09

Cardoso, J. L. (1998) Definição e detecção de zonas de acumulação de acidentes. Segurança Rodoviária - Avaliação e Redução da Sinistralidade, LNEC, Lisboa, 131-133.

Carlin, B. e Louis, T. (2000) Bayes and Empirical Bayes Methods for Data Analysis. Chapman and Hall, London.

ISBN: 1439840954

Cheng, W. e Washington, S. (2008) New Criteria for Evaluating Methods of Identifying Hot Spots. Transportation Research Record: Journal of the Transportation Research Board, 2083, 76-85.

DOI: 10.3141/2083-09

Cheng, W. e Washington, S. P. (2005) Experimental evaluation of hotspot identification methods. Accident Analysis & Prevention, 37, 870-881.

DOI: 10.1016/j.aap.2005.04.015

Elvik, R. (2008) Comparative Analysis of Techniques for Identifying Locations of Hazardous Roads. Transportation Research Record: Journal of the Transportation Research Board, 2083, 72-75.

DOI: 10.3141/2083-08

Ferreira, S. (2010) A Segurança Rodoviária no processo de planeamento de redes de transportes. Ph.D. dissertation, University of Porto.

Fridstrom, L., Ifver, J., Ingebrigtsen, S., Kulmala, R. e Thomsen, L. K. (1995) Measuring the contribution of randomness, exposure, weather and dayligth to the variation in road accident counts. Accident Analysis and Prevention, 27, 1-20.

DOI: 10.1016/0001-4575(94)E0023-E

Geedipally, S. R. e Lord, D. (2010) Identifying Hot Spots by Modeling Single-Vehicle and Multivehicle Crashes Separately Transportation Research Record: Journal of the Transportation Research Board, 2147, 97-104.

DOI: 10.3141/2147-12

Greene, W. H. (2008) Econometric Analysis. Sixth Edition. Pearson International Edition, New Jersey.

ISBN-13:978-0-13-513740-6; ISBN-10:0-13-513740-3

Greibe, P. (2003) Accident prediction models for urban roads. Accident Analysis and Prevention, 35, 273-285. DOI: 10.1016/S0001-4575(02)00005-2

Huang, H. e Haque, H. C. C. M. M. (2009) Empirical Evaluation of Alternative Approaches in Identifying Crash Hot Spots. Naive Ranking, Empirical Bayes, and Full Bayes Methods. Transportation Research Record: Journal of the Transportation Research Board, 2103, 32-41.

DOI: 10.3141/2103-05

Lan, B. e Persaud, B. (2011) Fully Bayesian Approach to Investigate and Evaluate Ranking Criteria for BlackSpot Identification. Transportation Research Record: Journal of the Transportation Research Board, 2237, 117-125.

DOI: 10.3141/2237-13

Lord, D. (2000) The prediction of accidents on digital networks: characteristics and issues related to the application of accident prediction models. Ph.D. dissertation, University of Toronto. http://hdl.handle.net/1807/14514

Lord, D. (2006) Modeling 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 and Prevention, 38, 751-766.

DOI: 10.1016/j.aap.2006.02.001

Lord, D. e Persaud, B. N. (2004) Estimating the safety performance of urban road transportation networks. Accident Analysis and Prevention, 36, 609-620.

DOI: 10.1016/S0001-4575(03)00069-1

Miranda-Moreno, L. F., Fu, L., Saccomanno, F. F. e Labbe, A. (2005) Alternative Risk Models for Ranking Locations for Safety Improvement. Transportation Research Record: Journal of the Transportation Research Board, 1908, 1-8.

DOI: 10.3141/1908-01

Montella, A. (2010) A comparative analysis of hotspot identification methods. Accident Analysis and Prevention, 42, 571-581.

DOI: 10.1016/j.aap.2009.09.025

OCDE (1997) Road safety principles and models: review of descriptive, predictive, risk and accident consequence models.Organisation for Economic Co-operation and Development, Paris. http://www.oecd.org/sti/transport/roadtransportresearch/2103285.pdf

Schluter, P. J., Deely, J. J. e Nicholson, A. J. (1997) Ranking and selecting motor vehicle accident sites by using a hierarchical Bayesian model. The Statistician, 46, 293-316.

DOI: 10.1111/1467-9884.00084

V. 21, n. 3, Dez. 2013

Downloads

Published

2013-11-14

How to Cite

Ferreira, S., & Couto, A. (2013). MÉTODO PROBABILÍSTICO PARA IDENTIFICAÇÃO DE ZONAS DE ACUMULAÇÃO DE ACIDENTES. TRANSPORTES, 21(3), 48–55. https://doi.org/10.4237/transportes.v21i3.683

Issue

Vol. 21 No. 3 (2013)

Section

Artigos

License

Authors who submit papers for publication by TRANSPORTES agree to the following terms:

  1. Authors retain copyright and grant TRANSPORTES the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  2. Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of this journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in TRANSPORTES.

  3. Authors are allowed and encouraged to post their work online (e.g., in institutional repositories or on their website) after publication of the article. Authors are encouraged to use links to TRANSPORTES (e.g., DOIs or direct links) when posting the article online, as TRANSPORTES is freely available to all readers.

  4. Authors have secured all necessary clearances and written permissions to published the work and grant copyright under the terms of this agreement. Furthermore, the authors assume full responsibility for any copyright infringements related to the article, exonerating ANPET and TRANSPORTES of any responsibility regarding copyright infringement.

  5. Authors assume full responsibility for the contents of the article submitted for review, including all necessary clearances for divulgation of data and results, exonerating ANPET and TRANSPORTES of any responsibility regarding to this aspect.