An integrated methodology for real-time driving risk status prediction using naturalistic driving data
出版年份 2021 全文链接
标题
An integrated methodology for real-time driving risk status prediction using naturalistic driving data
作者
关键词
Driving risk status prediction, Rolling time window approach, Naturalistic driving data, Car-following events, Machine learning algorithms
出版物
ACCIDENT ANALYSIS AND PREVENTION
Volume 156, Issue -, Pages 106122
出版商
Elsevier BV
发表日期
2021-04-24
DOI
10.1016/j.aap.2021.106122
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting
- (2020) Ke Wang et al. International Journal of Environmental Research and Public Health
- Real-time crash prediction on expressways using deep generative models
- (2020) Qing Cai et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Determining risk-based safety thresholds through naturalistic driving patterns using trajectory data on intercity expressways
- (2019) Narayana Raju et al. SAFETY SCIENCE
- Driving Safety Risk Prediction Using Cost-Sensitive With Nonnegativity-Constrained Autoencoders Based on Imbalanced Naturalistic Driving Data
- (2019) Jie Chen et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Forecasting Markers of Habitual Driving Behaviors Associated With Crash Risk
- (2019) George Panagopoulos et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Vehicle Driving Risk Prediction Based on Markov Chain Model
- (2018) Xiaoxia Xiong et al. DISCRETE DYNAMICS IN NATURE AND SOCIETY
- A Data-Driven Approach for Driving Safety Risk Prediction Using Driver Behavior and Roadway Information Data
- (2018) Nasim Arbabzadeh et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study
- (2018) Meixin Zhu et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Effects of crash warning systems on rear-end crash avoidance behavior under fog conditions
- (2018) Yina Wu et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- A Simulation Study of Predicting Real-Time Conflict-Prone Traffic Conditions
- (2017) Christos Katrakazas et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Real-Time Rear-End Collision-Warning System Using a Multilayer Perceptron Neural Network
- (2016) Donghoun Lee et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways
- (2015) Qi Shi et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction
- (2015) Lei Lin et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Bayesian Updating Approach for Real-Time Safety Evaluation with Automatic Vehicle Identification Data
- (2013) Mohamed M. Ahmed et al. TRANSPORTATION RESEARCH RECORD
- A data fusion framework for real-time risk assessment on freeways
- (2012) Mohamed Ahmed et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Development of crash prediction models with individual vehicular data
- (2011) Hojun “Daniel” Son et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
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