TY - GEN

T1 - An Adaptive Clustering Approach for Accident Prediction

AU - Dadwal, Rajjat

AU - Funke, Thorben

AU - Demidova, Elena

N1 - Funding Information: This work is partially funded by the BMWi, Germany under the projects “CampaNeo” (grant ID 01MD19007B), and “d-E-mand” (grant ID 01ME19009B), the European Commission (EU H2020, “smashHit”, grant-ID 871477) and DFG, German Research Foundation (“WorldKG”, DE 2299/2-1).

PY - 2021/9/19

Y1 - 2021/9/19

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85118448168&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2108.12308

DO - 10.48550/arXiv.2108.12308

M3 - Conference contribution

AN - SCOPUS:85118448168

SN - 978-1-7281-9143-0

T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

SP - 1405

EP - 1411

BT - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021

Y2 - 19 September 2021 through 22 September 2021

ER -