Avaliação da dependência espacial na modelagem do desempenho da segurança viária em zonas de tráfego
DOI:
https://doi.org/10.14295/transportes.v24i4.1110Keywords:
Road safety modeling, spatial autocorrelation, local spatial models.Abstract
A common technique used in the modeling process of the Road Safety Performance at the planning level is the Generalized Linear Models (GLM) procedure with the assumption of negative binomial error distribution. A main limitation of this technique, which is the no consideration of spatial effects, has been overcome by the use of local spatial models such as the Geographically Weighted Poisson Regression (GWPR). This work aims to present a comparative analysis between non spatial global and spatial local accident prediction models focused to estimate the safety performance of traffic accident zones of Fortaleza city. Models were calibrated to the dependent variable total accidents and accidents with victims and the results showed that GWPR models performed better than GLM on measures of adjustment and the reduction of residual spatial autocorrelation, being able to capture the spatial heterogeneity in the frequency of accidents.Downloads
References
Aguero-Valverde, Jonathan, (2013). Full Bayes Poisson gamma, Poisson lognormal, and zero inflated random effects models: Comparing the precision of crash frequency estimates. Accident Analysis & Prevention, v. 50, p. 289-297. http://dx.doi.org/10.1016/j.aap.2012.04.019.
Akaike, H. (1973). Information Theory and an Extension of the Maximum Likelihood Principle. In: B. N. PETROV and F. CSAKI, eds. Second International Symposium on Information Theory. Budapest: Akademiai Kiado, p. 267–281.
Almeida, E. (2012). Econometria Espacial Aplicada. Editora Alínea, Campinas, SP.
Ferreira, S. e Couto, A. (2012). Avaliação da Segurança Rodoviária em Fase de Planejamento: Modelo Estatístico de Resposta Qualitativa. Transportes, v.20, n.2, p. 48-56. DOI:10.4237/transportes.v20i2.548.
Fotheringham, A.S., Brunsdon, C., Charlton, M. (2002). Geographically Weighted Regression: the analysis of spatially varying relationships. Wiley, New York.
Gomes, M. J. T. L., Torres, C. A., Oliveira Neto, F. M. e Cunto, F. J. C. (2015). Análise exploratória para a modelagem da frequência de acidentes de trânsito agregados ao nível de zonas de tráfego. Transportes, v.23, n.4, p. 42-50. DOI: 10.14295/transportes.v23i4.927.
Hadayeghi, A., Shalaby, A.S., Persaud, B. (2003). Macro-level accident prediction models for evaluating safety of urban transportation systems. Transportation Research Record, v. 1840, n. 1, p. 87-95. DOI: 10.3141/1840-10.
Hadayeghi, A., Shalaby, A. and Persaud, B. (2010). Development of planning level transportation safety tools using Geographically Weighted Poisson Regression. Accident Analysis and Prevention, v. 42, p. 676-688. DOI:10.1016/j.aap.2009.10.016.
Hauer, E (2002). Observational Before-after Studies in Road Safety. (1a ed.). Pergamon. Toronto, Canadá.
Lord, D. and Persaud, B. N. (2004). Estimating the safety performance of urban road transportation networks. Accident Analysis and Prevention, v. 36, n. 4, p. 609-620. DOI: 10.1016/S0001-4575(03)00069-1.
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, v. 38, n. 4, p. 751-766. DOI:10.1016/j.aap.2006.02.001.
Oh, J., Lyon, C., Washington, S., Persaud, B., Bared, J. (2003). Validation of FHWA crash models for rural intersection lessons learned. Transportation Research Record, v. 1840, p. 41–49. http://dx.doi.org/10.3141/1840-05.
Matkan, A. A., Mohaymany, A. S., Mirbagheri, B., Shahri, M. (2011). Explorative spatial analysis of traffic accidents using GWPR model for urban safety planning. 3rd International Conference on Road Safety and Simulation, September 14-16, 2011, Indianapolis, USA.
Nakaya, T., Fotheringham, A.S., Brunsdon, C. e Charlton, M. (2005). Geographically weighted poisson regression for disease association mapping. Statistics in Medicine, v.24, p. 2695–2717. DOI: 10.1002/sim.2129.
Silva, A. R. e Fotheringham, A. S. (2016). The Multiple Testing Issue in Geographically Weighted Regression. Geographical Analysis. v.48, p.233-247. Doi: 10.1111/gean.12084.
Silva, A. R. da, Rodrigues, T. C. V. (2014). Geographically weighted negative binomial regression-incorporating overdispersion. Statistics and Computing, v.24, p. 769-783. DOI 10.1007/s11222-013-9401-9.
Silva, A. R. da, Rodrigues, T. C. V. (2016). A SAS® macro for geographically weighted negative binomial regression. Disponível em: <http://support.sas.com/resources/papers/proceedings16/8000-2016.pdf>. Data de acesso: 01/06/2016.
Tarko, A. P., Inerowicz, M., Ramos, J. and Li, W. (2008). Tool with road-level crash prediction for transportation safety planning. Transportation Research Record: Journal of Transportation Research Board, v. 2083, n. 1, p. 16-25. DOI:10.3141/2083-03.
Wheeler, D., Calder, C. (2007). An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. Journal of Geographical Systems, v.9, p. 145–166. DOI 10.1007/s10109-006-0040-y.
Zeng, Q. e Huang, H. (2014). Bayesian spatial joint modeling of traffic crashes on an urban road network. Accident Analysis & Prevention, v. 67, p. 105-112. http://dx.doi.org/10.1016/j.aap.2014.02.018.
Zhao, F., Chow, L., Li, M., Liu, X., 2005. A Transit ridership model based on geographically weighted regression and service quality variables. Report DO97591, Lehman Center for Transportation Research, Department of Civil and Environmental Engineering, Florida International University.
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