Abstract (EN):
This paper presents a probabilistic approach to measure the potential to reduce crash frequency
of urban segments. This new approach leads to a hotspot definition and identification using a
probabilistic model defining the dependent variable as an indicator of a discrete choice. A binary
choice model is used considering a binary dependent variable that differentiates a hotspot from a
safe site set by the number of crashes per kilometre. The explanatory variables to set similar
segments are based on average annual daily traffic, segment length, density of minor
intersections. A threshold value for the number of crashes per kilometre is set to distinguish
hotspots from safe sites. Based on this classification, a binary model is applied that allows the
construction of an ordered site list using the probability of a site being a hotspot. A
demonstration of the proposed methodology is provided using simulated data. For the simulation
design, urban segment data from Porto, Portugal, covering a five-year period are used. The
results of the binary model show a good fit. To evaluate and compare the probabilistic method
with other used methods described in the Highway Safety Manual, measures are used to test the
performance of each method in terms of its power to detect the “true” hotspots. As already
demonstrated through the application of the binary model to urban intersections, the test results
indicate that the binary model performs better than the other two models. The gains of using this
method are the simplicity, the reliability, and the efficiency.
Language:
English
Type (Professor's evaluation):
Scientific