Using Vehicle Data to Enhance Prediction of Accident-Prone Areas-Reference-Cited by-同舟云学术

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Using Vehicle Data to Enhance Prediction of Accident-Prone Areas

Published:2022-10-08 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
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Author:

Wowo Kelvin Sopnan¹,Dadwal Rajjat²,Graen Timo¹,Fiege Andrea¹,Nolting Michael¹,Nejdl Wolfgang²,Demidova Elena³,Funke Thorben²

Affiliation:

1. Volkswagen AG,Hanover,30419

2. L3S Research Center, Leibniz University of Hanover,Hanover,Germany,30167

3. University of Bonn,Data Science & Intelligent Systems Group,Bonn,Germany,53115

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9921415/9921739/09922236.pdf?arnumber=9922236

Reference28 articles.

1. The Analysis of UK Road Traffic Accident Data and its Use in the Development of a Direct Vision Standard for Trucks in London

2. A Multilevel Graph Approach for Road Accidents Data Interpretation

3. Road crashes analysis and prediction using gradient boosted and random forest trees;elyassami;2020 6th IEEE Congress on Information Science and Technology (CiSt) IEEE,2021

4. Extracting Hidden Patterns Within Road Accident Data Using Machine Learning Techniques

5. A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Road Safety: Predictive Modeling of Accident-Prone Zones with ADAS-Equipped Vehicle Fleet Data;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

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