Bayesian road safety analysis: incorporation of past evidence and effect of hyper-prior choice.
OBJECTIVE
This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites).


METHODS
A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas.


RESULTS
This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis.


CONCLUSIONS
As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification.


CONCLUSIONS
The results show the benefits of incorporating prior information when working with limited crash data in road safety studies.
Department of Civil Engineering and Applied Mechanics, McGill University, Macdonald Engineering Building, 817 Sherbrooke St. W., Montreal, Quebec H3A 2K6, Canada. luis.miranda-moreno@mcgill.ca
Luis F
LF
Miranda-Moreno
Shahram
S
Heydari
Dominique
D
Lord
Liping
L
Fu
D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data* / Q000639:trends; D001499:Bayes Theorem; D002140:California; D003198:Computer Simulation; D003430:Cross-Sectional Studies; D006801:Humans; D015233:Models, Statistical; D055361:Multilevel Analysis; D019982:Organizational Case Studies; D016012:Poisson Distribution; D018570:Risk Assessment / Q000379:methods*; D012424:Rural Population / Q000706:statistics & numerical data; D012449:Safety / Q000706:statistics & numerical data*; D013781:Texas
Bayesian approach; hyper-prior assumptions; past evidence; road safety