Evaluation of Multivariate Poisson Log Normal Bayesian Methods for Before-After Road Safety Evaluations

The multivariate Poisson log normal (MVPLN) Bayesian method has recently been introduced in road safety analysis mainly for network screening and using different crash severity levels. However, there is little or no research applying MVPLN to different crash types. Besides, only one model structure for the expected crashes of a given severity was investigated in previous MVPLN studies. Another knowledge gap is that this method has not yet been evaluated for before–after treatment effect analysis. The objective of this study was to evaluate the application of MVPLN Bayesian method for before–after road safety evaluation studies. Two groups of unsignalized California intersections, for which a naive before–after comparison shows a significant change in the crash frequency after a hypothetical treatment was assigned, were used to conduct the study. It was found that the crash reduction rates are sensitive to the function form of expected crashes. For each model structure, MVPLN, univariate Poisson log normal (PLN) and Poisson-gamma models provided comparable results while PLN was seen to be superior. Finally, models that consider temporal effects of unobserved latent variables were found to be superior to those that don’t.

[1]  Bhagwant Persaud,et al.  Validation of a Full Bayes methodology for observational before-after road safety studies and application to evaluation of rural signal conversions. , 2009, Accident; analysis and prevention.

[2]  Michael D. Pawlovich,et al.  Iowa's Experience with Road Diet Measures: Use of Bayesian Approach to Assess Impacts on Crash Frequencies and Crash Rates , 2006 .

[3]  K. Kockelman,et al.  Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity , 2006 .

[4]  Peter Congdon,et al.  Bayesian Models for Categorical Data: Peter Congdon/Bayesian Models for Categorical Data , 2006 .

[5]  Wen Li,et al.  Iowa's Experience with Road Diet Measures , 2006 .

[6]  Efthymios G. Tsionas,et al.  BAYESIAN MULTIVARIATE POISSON REGRESSION , 2001 .

[7]  Stephen P. Brooks,et al.  Markov chain Monte Carlo method and its application , 1998 .

[8]  K. El-Basyouny,et al.  Collision prediction models using multivariate Poisson-lognormal regression. , 2009, Accident; analysis and prevention.

[9]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[10]  A. Linde.  DIC in variable selection , 2005 .

[11]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .

[12]  Michael Pawlovich,et al.  FROM EMPIRICAL BAYES TO FULL BAYES: METHODS FOR ANALYZING TRAFFIC SAFETY DATA , 2004 .

[13]  Bhagwant Persaud,et al.  Disaggregate Safety Performance Models for Signalized Intersections on Ontario Provincial Roads , 1998 .

[14]  A. Vogt.  CRASH MODELS FOR RURAL INTERSECTIONS: FOUR-LANE BY TWO-LANE STOP-CONTROLLED AND TWO-LANE BY TWO-LANE SIGNALIZED , 1999 .

[15]  Paul Damien,et al.  A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. , 2008, Accident; analysis and prevention.

[16]  Luis F. Miranda-Moreno,et al.  Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-gamma models for modeling motor vehicle crashes: a Bayesian perspective , 2008 .

[17]  Simon Washington,et al.  A simultaneous equations model of crash frequency by collision type for rural intersections , 2009 .

[18]  Tarek Sayed,et al.  Accident Prediction Models for Urban Unsignalized Intersections in British Columbia , 1999 .

[19]  Loukia Meligkotsidou,et al.  Multivariate Poisson regression with covariance structure , 2005, Stat. Comput..

[20]  Nairanjana Dasgupta,et al.  Bayesian Models for Categorical Data , 2007, Technometrics.

[21]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[22]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

[23]  Eun Sug Park,et al.  A fully Bayesian multivariate approach to before-after safety evaluation. , 2010, Accident; analysis and prevention.

[24]  Paul P Jovanis,et al.  Bayesian Multivariate Poisson Lognormal Models for Crash Severity Modeling and Site Ranking , 2009 .

[25]  Aki Vehtari.  Discussion to "Bayesian measures of model complexity and fit" by Spiegelhalter, D.J., Best, N.G., Carlin, B.P., and van der Linde, A. , 2002 .

[26]  S. E. Ahmed,et al.  Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference , 2008, Technometrics.

[27]  Eun Sug Park,et al.  Multivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity , 2007 .

[28]  Bhagwant Persaud,et al.  Comparison of empirical Bayes and full Bayes approaches for before-after road safety evaluations. , 2010, Accident; analysis and prevention.

[29]  Bhagwant Persaud,et al.  Calibration and Transferability of Accident Prediction Models for Urban Intersections , 2002 .

[30]  Ezra Hauer,et al.  Observational Before-After Studies in Road Safety , 1997 .

[31]  Bhagwant Persaud,et al.  Empirical Bayes before-after safety studies: lessons learned from two decades of experience and future directions. , 2007, Accident; analysis and prevention.