Investigating Roundabout Properties and Bicycle Accident Occurrence at Swiss Roundabouts: A Logistic Regression Approach

Hollenstein, Daria; Heß, Martin; Jordan, Denis; Bleisch, Susanne

doi:10.3390/ijgi8020095

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Yuan et al [10] predicted the accident occurrence for each road segment in the state of Iowa each hour in similar work. Hollenstein et al [11] investigated the association of bicycle accident occurrence to roundabout properties of the road at Swiss roundabouts using a logistic regression approach. The authors also studied various features of roundabouts responsible for bicycle accidents.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Clustering Approach for Accident Prediction

Dadwal¹,

Funke²,

Demidova³

2021

Preprint

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Traffic accident prediction is a crucial task in the mobility domain. State-of-the-art accident prediction approaches are based on static and uniform grid-based geospatial aggregations, limiting their capability for fine-grained predictions. This property becomes particularly problematic in more complex regions such as city centers. In such regions, a grid cell can contain subregions with different properties; furthermore, an actual accident-prone region can be split across grid cells arbitrarily. This paper proposes Adaptive Clustering Accident Prediction (ACAP) -a novel accident prediction method based on a grid growing algorithm. ACAP applies adaptive clustering to the observed geospatial accident distribution and performs embeddings of temporal, accidentrelated, and regional features to increase prediction accuracy. We demonstrate the effectiveness of the proposed ACAP method using open real-world accident datasets from three cities in Germany. We demonstrate that ACAP improves the accident prediction performance for complex regions by 2-3 percent points in F1-score by adapting the geospatial aggregation to the distribution of the underlying spatio-temporal events. Our grid growing approach outperforms the clustering-based baselines by four percent points in terms of F1-score on average.

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Section: Related Workmentioning

confidence: 99%