@proceedings{dadwal2021adaptive,
  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. },
  added-at = {2022-02-18T12:12:17.000+0100},
  author = {Dadwal, Rajjat and Funke, Thorben and Demidova, Elena},
  biburl = {https://www.bibsonomy.org/bibtex/22585f64ce91587144e9679ae4ca70d58/dadwal},
  doi = {10.1109/ITSC48978.2021.9564564},
  eventdate = {19-22 Sept. 2021},
  eventtitle = {The 24th IEEE Intelligent Transportation Systems Conference (ITSC 2021)},
  interhash = {596e726043e8513f4b281c611ba7fb4f},
  intrahash = {2585f64ce91587144e9679ae4ca70d58},
  isbn = {Electronic ISBN:978-1-7281-9142-3},
  keywords = {myown},
  language = {English},
  publisher = {IEEE},
  timestamp = {2022-02-18T14:02:46.000+0100},
  title = {An Adaptive Clustering Approach for Accident Prediction},
  year = 2021
}

@inproceedings{conf/itsc/DadwalFD21,
  added-at = {2021-11-03T00:00:00.000+0100},
  author = {Dadwal, Rajjat and Funke, Thorben and Demidova, Elena},
  biburl = {https://www.bibsonomy.org/bibtex/2f77abe23df3e5c7f08ae474141cbc8c7/dblp},
  booktitle = {ITSC},
  crossref = {conf/itsc/2021},
  ee = {https://doi.org/10.1109/ITSC48978.2021.9564564},
  interhash = {596e726043e8513f4b281c611ba7fb4f},
  intrahash = {f77abe23df3e5c7f08ae474141cbc8c7},
  isbn = {978-1-7281-9142-3},
  keywords = {dblp},
  pages = {1405-1411},
  publisher = {IEEE},
  timestamp = {2024-04-09T23:29:22.000+0200},
  title = {An Adaptive Clustering Approach for Accident Prediction.},
  url = {http://dblp.uni-trier.de/db/conf/itsc/itsc2021.html#DadwalFD21},
  year = 2021
}

@proceedings{dadwal2021adaptive,
  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. },
  added-at = {2021-07-31T20:14:27.000+0200},
  author = {Dadwal, Rajjat and Funke, Thorben and Demidova, Elena},
  biburl = {https://www.bibsonomy.org/bibtex/22a6305fb5db1f65c371974b60bbaf21c/demidova},
  booktitle = {Proc. of the 24th {IEEE} International Intelligent Transportation Systems Conference, {ITSC} 2021},
  doi = {10.1109/ITSC48978.2021.9564564},
  interhash = {596e726043e8513f4b281c611ba7fb4f},
  intrahash = {2a6305fb5db1f65c371974b60bbaf21c},
  keywords = {campaneo dadwal demand funke myown smashhit worldkg},
  publisher = {IEEE},
  timestamp = {2022-02-27T13:52:40.000+0100},
  title = {An Adaptive Clustering Approach for Accident Prediction},
  year = 2021
}