Advantages and disadvantages of the previous traditional methods for identifying crash-prone sections [30–33].
Method
Advantage
Disadvantage
Crash frequency (CF)
It is the simplest identifying method. It performs better than other methods with more appealing theoretical arguments.
Large quantities of random road traffic accident data are needed, and it is difficult to explain the contributing factors in this manner. Crash count does not always give an unbiased estimate of the long-term expected number of crashes because crash counts can randomly fluctuate during the observation period.
Equivalent property damage only crash frequency
It measures weights crashes according to the severity (fatal, injury and property damage only) to develop a combined frequency and severity score for each site.
Large quantity of random road traffic accident data are needed, and it is difficult for explaining the contributing factors in this manner. Crash count does not always give an unbiased estimate of the long-term expected number of crashes because crash counts can randomly fluctuate during the observation period.
Crash rate (CR)
It normalizes the crash frequency with exposure measured by traffic volume. Road segment traffic volume is measured as vehicle-kilometers travelled for the study period and this method reflects crash risk for the individual road user.
Large quantity of random road traffic accident data and the corresponding traffic volume data are needed, and it is difficult for explaining the contributing factors using this way. Crash count does not always give an unbiased estimate of the long-term expected number of crashes because crash counts can randomly fluctuate during the observation period.
Empirical Bayesian approach (EB)
It can control the random fluctuations in the recorded number of crashes. It can overcome the limitations of the conventional methods by accounting not only for regression-to-the-mean effects, but also for traffic volume changes and for time trends in accident occurrence due to changes over time in factors such as weather, accident reporting practices and driving habits.
Large quantity of random road traffic accident data and the corresponding detailed data are needed as well.
