Identifying traffic accident black spots with Poisson-Tweedie models. (February 2018)

• Record Type: Journal Article
• Authors: Debrabant, Birgit; Halekoh, Ulrich; Bonat, Wagner Hugo; Hansen, Dennis L.; Hjelmborg, Jacob; Lauritsen, Jens
• Is Part Of: Accident analysis and prevention. Volume 111(2018)
• Page Start: 147
• Page End: 154
• Publication Date: 2018-02
• Subjects: Black spot detection -- Traffic accidents -- Hospital admission data -- Poisson–Tweedie distribution; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2017.11.021
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Traffic black spot identification based on hospital admission data. Modelling with the flexible class of Poisson–Tweedie distributions. Fast and easily applicable fitting algorithm accessible via open access software. 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.