Identification and Ranking of Black Spots: Sensitivity Analysis

Abstract

In Flanders, Belgium, approximately 1,014 accident locations are considered dangerous. These dangerous accident sites, or black spots, are selected by means of historic accident records for the period 1997 to 1999. A combination of weighting values, respectively, 1 for each light injury, 3 for each serious injury, and 5 for each deadly injury (1_3_5), is used to rank and select the most dangerous accident locations. A sensitivity analysis was performed to investigate the effect on the identification and ranking of black spots as based on three different weighting value combinations, representing a different attitude toward the traffic safety problem: avoiding all accidents (1_1_1), all deadly accidents (1_1_10), and all accidents with serious or deadly injuries (1_10_10). Furthermore, effects of use of the expected number of accidents, estimated from a hierarchical Bayesian model, instead of the historic count data, to rank and select the accidents sites, were evaluated. Results show that a different attitude toward the traffic safety problem and the choice of the corresponding injury weighting values on the one hand and use of estimates instead of count values on the other hand do have important consequences for the selection and ranking of black spots. This is important not only for the number of accident locations that will receive a different ranking order but also for the effect on the type of accident locations that are selected as dangerous and accordingly for the resulting future traffic safety decisions.

Ranking and selecting dangerous crash locations: Correcting for the number of passengers and Bayesian ranking plots

“…In accordance with our previous research (Geurts et al, 2004), we will use the percentage deviation value to quantify the effects changing the ranking and selection criteria of dangerous accident locations. This measure allows comparing the rankings of two datasets containing different locations.…”

“…One of the advantages of this MCMC method is that it allows for exploring posterior distributions of a certain function related to the parameters of interest. Results (Geurts et al, 2004) showed that the use of these Bayesian estimation values from this model instead of historic count data to rank the accident locations indeed can overcome the problem of random variation in accident counts and will have an important effect on the selection of the most dangerous accident locations.…”

“…In a previous paper, a sensitivity analysis was carried out to investigate the strengths and weaknesses of the method that is currently used in Flanders (the Flemish speaking community of Belgium) to identify and rank dangerous accident locations (Geurts et al, 2004). These accident sites are selected by the Flemish government by means of their historic accident data for the period 1997-1999.…”

“…Results in the previous paper (Geurts et al, 2004) showed that a change in the traffic safety policy and the reflection of this choice in the injury weighting values used to identify and rank the most dangerous accident locations will not only have an important impact on the number of sites that will change when selecting and ranking accident locations.…”

“…Government should therefore carefully decide which priorities should be stressed in the traffic safety policy (for more details on the discussion of the implications of the use of 1_3_5 weighting values see Geurts et al (2004)). …”