Developing a new real-time traffic safety management framework for urban expressways utilizing reinforcement learning tree

Keyphrases

• Real-time Traffic 100%
• Management Framework 100%
• Urban Expressway 100%
• Traffic Safety Management 100%
• Real-time Crash Prediction 100%
• Reinforcement Learning Tree 100%
• Crash Detection 60%
• Detection Model 60%
• Performance Prediction 60%
• Automatic Incident Detection 60%
• Deep Neural Network 40%
• Logistic Regression 40%
• Crash 40%
• Regression Support Vector Machine 40%
• Crash Risk Prediction Model 40%
• Modeling Techniques 20%
• Sensitivity Analysis 20%
• False Alarm Rate 20%
• Proactive Measures 20%
• Traffic Data 20%
• Negative Impact 20%
• Disruptive Technology 20%
• Online Traffic 20%
• Urban Traffic Control System 20%
• Traffic Dynamics 20%
• Variable Effect 20%
• Crash Precursor 20%
• Traffic Flow Data 20%
• Minimum Sample Size 20%
• Design Countermeasures 20%
• Crash Data 20%
• Tree Model 20%
• Crash Frequency 20%
• Smooth Traffic 20%
• Traffic Fluctuation 20%
• Traffic Crashes 20%
• Result Prediction 20%

Computer Science

• Reinforcement Learning 100%
• Management Framework 100%
• Predictive Performance 60%
• Support Vector Machine 40%
• Deep Neural Network 40%
• Prediction Model 40%
• Logistic Regression 40%
• Large Data Set 20%
• Data Streaming 20%
• Dynamic Traffic 20%
• Disruptive Technology 20%
• Online Traffic 20%
• Traffic Control System 20%
• Negative Impact 20%
• False Alarm Rate 20%