An Adaptive Clustering Approach for Accident Prediction

TL;DR

This paper introduces ACAP, an adaptive clustering method for accident prediction that improves accuracy in complex urban regions by dynamically adjusting geospatial aggregations based on accident distributions.

Contribution

The paper presents a novel adaptive clustering approach for accident prediction that outperforms traditional grid-based methods, especially in complex city regions.

Findings

ACAP improves F1-score by 2-3 percentage points in complex regions.

ACAP outperforms baseline clustering methods by 4 percentage points in F1-score.

Adaptive clustering enhances prediction accuracy in heterogeneous urban areas.

Abstract

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, accident-related, 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.