Automated machine learning for analysis and prediction of vehicle crashes

Keyphrases

• AutoML 100%
• Vehicle Crash 100%
• New York City 66%
• Machine Learning Models 66%
• Geographic Region 33%
• Politicians 33%
• City Scale 33%
• Network Technology 33%
• Graphical User Interface 33%
• Support Vector Machine Learning 33%
• Database Technology 33%
• Road Segment 33%
• Code Level 33%
• Code-based 33%
• Machine Learning Approach 33%
• Prediction Approach 33%
• User-friendly Interface 33%
• Vehicle Traffic 33%
• Crash Data 33%
• City Car 33%
• Injury Prediction 33%
• Collision Risk 33%
• Zip Code 33%
• Segment Type 33%
• Postal Code 33%
• Insurance Business 33%
• Accident Causes 33%
• Visualization Approaches 33%
• Traffic Injury 33%
• Traffic Fatalities 33%
• Fatality Prediction 33%
• Risk Data 33%

Computer Science

• Automated Machine Learning 100%
• Machine Learning 100%
• Learning System 100%
• Collected Data 50%
• Networking Technology 50%
• Graphical Interface 50%
• Support Vector Machine 50%
• Machine Learning Approach 50%
• Friendly Interface 50%
• Geographic Region 50%
• Future Incident 50%

Engineering

• Learning System 100%
• Collected Data 25%
• Road 25%
• Cutting Edge 25%
• Learning Approach 25%
• Collision Risk 25%
• Policymakers 25%
• Networking Technology 25%
• Support Vector Machine 25%