Severity Prediction of Traffic Accidents with Recurrent Neural Networks
Published 2017 View Full Article
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Title
Severity Prediction of Traffic Accidents with Recurrent Neural Networks
Authors
Keywords
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Journal
Applied Sciences-Basel
Volume 7, Issue 6, Pages 476
Publisher
MDPI AG
Online
2017-06-08
DOI
10.3390/app7060476
References
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Related references
Note: Only part of the references are listed.- Traffic accident severity prediction using a novel multi-objective genetic algorithm
- (2017) Seyed Hessam-Allah Hashmienejad et al. INTERNATIONAL JOURNAL OF CRASHWORTHINESS
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- (2016) Maher Ibrahim Sameen et al. Geomatics Natural Hazards & Risk
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
- (2014) Wenhao Huang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Traffic Flow Prediction With Big Data: A Deep Learning Approach
- (2014) Yisheng Lv et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- A System for Automatic Notification and Severity Estimation of Automotive Accidents
- (2013) Manuel Fogue et al. IEEE TRANSACTIONS ON MOBILE COMPUTING
- Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models
- (2013) Chengcheng Xu et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Signal reconstruction, modeling and simulation of a vehicle full-scale crash test based on Morlet wavelets
- (2012) Hamid Reza Karimi et al. NEUROCOMPUTING
- Mathematical modeling and parameters estimation of a car crash using data-based regressive model approach
- (2011) Witold Pawlus et al. APPLIED MATHEMATICAL MODELLING
- Application of viscoelastic hybrid models to vehicle crash simulation
- (2011) Witold Pawlus et al. INTERNATIONAL JOURNAL OF CRASHWORTHINESS
- Mathematical modeling of a vehicle crash test based on elasto-plastic unloading scenarios of spring-mass models
- (2010) Witold Pawlus et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Noise injection for training artificial neural networks: A comparison with weight decay and early stopping
- (2009) Richard M. Zur et al. MEDICAL PHYSICS
- Application of generalized link functions in developing accident prediction models
- (2009) Karim El-Basyouny et al. SAFETY SCIENCE
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