Safety performance functions with measurement errors in traffic volume

One of the most common and important predictors in safety performance functions (SPFs) is traffic volume which is known to be measured with uncertainty. Such measurement errors (ME) can attenuate the respective predictors’ effect and also increase dispersion. This paper proposes an approach which involves the use of a ME model based on traffic flow time series data. The model is used in conjunction with the negative binomial SPF to circumvent the bias in predicting the aggregate number of accidents during the time period under study. The proposed approach (denoted by MENB), was compared with the traditional negative binomial (NB) technique by way of Monte Carlo simulation. Furthermore, both approaches were applied to two datasets corresponding to 131 and 130 road segments in British Columbia. The full Bayes method was utilized for parameter estimation, performance evaluation and inference through the use of Markov Chain Monte Carlo (MCMC) techniques. The simulation results showed that MENB has outperformed NB when large measurement errors are present. The goodness-of-fit statistics showed that MENB has provided a slightly better fit to the data. However, in the presence of measurement errors, the NB has underestimated the predicted number of accidents for heavy traffic on long road segments and vice versa. The use of MENB is justified when the variance in volume between years is large otherwise both approaches yield comparable results.