Citation

Debrabant B, Halekoh U, Bonat WH, Hansen DL, Hjelmborg J, Lauritsen J. Accid. Anal. Prev. 2018; 111: 147-154.

Affiliation

Accident Analysis Group, Department of Ortopedics, Odense University Hospital, Odense, Denmark; Department of Clinical Medicine, University of Southern Denmark, Odense, Denmark.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.aap.2017.11.021

PMID

29202323

Abstract

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.

Copyright © 2017 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Black spot detection; Hospital admission data; Poisson–Tweedie distribution; Traffic accidents