TY  - JOUR
PY  - 2018//
TI  - Identifying traffic accident black spots with Poisson-Tweedie models
JO  - Accident analysis and prevention
A1  - Debrabant, Birgit
A1  - Halekoh, Ulrich
A1  - Bonat, Wagner Hugo
A1  - Hansen, Dennis L.
A1  - Hjelmborg, Jacob
A1  - Lauritsen, Jens
SP  - 147
EP  - 154
VL  - 111
IS  - 
N2  - This paper aims at the identification of black spots for traffic accidents, i.e. locations with accident counts beyond what is usual for similar locations, using spatially and temporally aggregated hospital records from Funen, Denmark. Specifically, we apply an autoregressive Poisson-Tweedie model, which covers a wide range of discrete distributions and handles zero-inflation as well as overdispersion. The estimated power parameter of the model was 1.6 (SE=0.06) suggesting a distribution close to the Pólya-Aeppli distribution. We identified nine black spots consistently standing out in all six considered calendar years and calculated by simulations a probability of p=0.03 for these to be chance findings. Altogether, our results recommend these sites for further investigation and suggest that our simple approach could play a role in future area based traffic accident prevention planning.<br><br>Copyright © 2017 Elsevier Ltd. All rights reserved.<p />  <p>Language: en</p>
LA  - en
SN  - 0001-4575
UR  - http://dx.doi.org/10.1016/j.aap.2017.11.021
ID  - ref1
ER  -