Details
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1405-1411 |
| Number of pages | 7 |
| ISBN (electronic) | 9781728191423 |
| ISBN (print) | 978-1-7281-9143-0 |
| Publication status | Published - 19 Sept 2021 |
| Event | 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 - Indianapolis, United States Duration: 19 Sept 2021 → 22 Sept 2021 |
Publication series
| Name | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC |
|---|---|
| Volume | 2021-September |
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.
ASJC Scopus subject areas
- Engineering(all)
- Automotive Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
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2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1405-1411 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2021-September).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
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 -