Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 2450-2456 |
| Seitenumfang | 7 |
| ISBN (elektronisch) | 9781665468800 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, China Dauer: 8 Okt. 2022 → 12 Okt. 2022 |
Publikationsreihe
| Name | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC |
|---|---|
| Band | 2022-October |
Abstract
Detection of accident-prone areas in road networks is crucial to increase road safety and reduce accident risk. Traditionally, statistical methods and, more recently, neural networks have been applied to identify accident-prone road network areas and reduce crash rates. However, these approaches rely on historical accident data as input, often unavailable in urban regions. Furthermore, nearly accidents are not part of statistical accident records; however, vehicle braking and acceleration data can reveal risk areas with frequent nearly accidents. In this paper, we propose a novel risk area detection approach, which examines an entire city and detects accident prone-areas by adopting a convolutional neural network to vehicle data. This deep learning method leverages various features extracted from car fleet data, including acceleration and braking signals, together with traffic lights, road networks, and point-of-interest data. We evaluate our approach against several established machine learning algorithms, including linear regression, support vector machines, and an artificial neural network. Our experiments on real-world data demonstrate that the proposed approach outperforms the baselines in discriminating accident-prone areas from safer ones in terms of accuracy, precision, recall, and F1 score.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Fahrzeugbau
- Ingenieurwesen (insg.)
- Maschinenbau
- Informatik (insg.)
- Angewandte Informatik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. S. 2450-2456 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Band 2022-October).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Using Vehicle Data to Enhance Prediction of Accident-Prone Areas
AU - Wowo, Kelvin Sopnan
AU - Dadwal, Rajjat
AU - Graen, Timo
AU - Fiege, Andrea
AU - Nolting, Michael
AU - Nejdl, Wolfgang
AU - Demidova, Elena
AU - Funke, Thorben
N1 - Funding Information: This work was partially funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) under the project “CampaNeo” (grant ID 01MD19007A, 01MD19007B).
PY - 2022
Y1 - 2022
N2 - Detection of accident-prone areas in road networks is crucial to increase road safety and reduce accident risk. Traditionally, statistical methods and, more recently, neural networks have been applied to identify accident-prone road network areas and reduce crash rates. However, these approaches rely on historical accident data as input, often unavailable in urban regions. Furthermore, nearly accidents are not part of statistical accident records; however, vehicle braking and acceleration data can reveal risk areas with frequent nearly accidents. In this paper, we propose a novel risk area detection approach, which examines an entire city and detects accident prone-areas by adopting a convolutional neural network to vehicle data. This deep learning method leverages various features extracted from car fleet data, including acceleration and braking signals, together with traffic lights, road networks, and point-of-interest data. We evaluate our approach against several established machine learning algorithms, including linear regression, support vector machines, and an artificial neural network. Our experiments on real-world data demonstrate that the proposed approach outperforms the baselines in discriminating accident-prone areas from safer ones in terms of accuracy, precision, recall, and F1 score.
AB - Detection of accident-prone areas in road networks is crucial to increase road safety and reduce accident risk. Traditionally, statistical methods and, more recently, neural networks have been applied to identify accident-prone road network areas and reduce crash rates. However, these approaches rely on historical accident data as input, often unavailable in urban regions. Furthermore, nearly accidents are not part of statistical accident records; however, vehicle braking and acceleration data can reveal risk areas with frequent nearly accidents. In this paper, we propose a novel risk area detection approach, which examines an entire city and detects accident prone-areas by adopting a convolutional neural network to vehicle data. This deep learning method leverages various features extracted from car fleet data, including acceleration and braking signals, together with traffic lights, road networks, and point-of-interest data. We evaluate our approach against several established machine learning algorithms, including linear regression, support vector machines, and an artificial neural network. Our experiments on real-world data demonstrate that the proposed approach outperforms the baselines in discriminating accident-prone areas from safer ones in terms of accuracy, precision, recall, and F1 score.
UR - http://www.scopus.com/inward/record.url?scp=85141824471&partnerID=8YFLogxK
U2 - 10.1109/ITSC55140.2022.9922236
DO - 10.1109/ITSC55140.2022.9922236
M3 - Conference contribution
AN - SCOPUS:85141824471
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2450
EP - 2456
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Y2 - 8 October 2022 through 12 October 2022
ER -