A framework for real-time traffic risk prediction incorporating cost-sensitive learning and dynamic thresholds

Engineering

• Risk Prediction 100%
• Genetic Algorithm 66%
• Risk Level 66%
• Computation Time 66%
• Misclassification 66%
• Prediction Performance 33%
• Performance Degradation 33%
• Deep Learning Method 33%
• Safety Management 33%
• Real Vehicle 33%
• Local Optimum 33%
• Granularity 33%

Keyphrases

• Learning Thresholds 100%
• Learning Dynamics 40%
• Cost Threshold 40%
• Cost Coefficient 40%
• Multi-class Prediction 40%
• Misclassification Cost 20%
• Real Vehicle 20%
• Convergence Curve 20%
• Real-time Risk 20%

Computer Science

• Cost Coefficient 100%
• Granularity Level 50%