A deep reinforcement learning-based intelligent intervention framework for real-time proactive road safety management.

We propose a variable speed limit (VSL) system for improving the safety of urban expressways in real time. The system has two main functions: monitoring traffic data and then using the data to assess crash risk through a real-time crash prediction model (RTCPM). When the risk is high, the system triggers VSL control to restore traffic conditions to normal.

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements.

Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent advancements in artificial intelligence, sensor fusion and algorithms have brought about the introduction of a proactive safety management system closer to reality. The basic prerequisite for developing such a system is to have a reliable crash prediction model that takes real-time traffic data as input and evaluates their association with crash risk.

Understanding crash mechanism on urban expressways using high-resolution traffic data.

Urban expressways play a vital role in the modern mega cities by serving peak hour traffic alongside reducing travel time for moderate to long distance intra-city trips. Thus, ensuring safety on these roads holds high priority. Little knowledge has been acquired till date regarding crash mechanism on these roads.

A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways.

The concept of measuring the crash risk for a very short time window in near future is gaining more practicality due to the recent advancements in the fields of information systems and traffic sensor technology. Although some real-time crash prediction models have already been proposed, they are still primitive in nature and require substantial improvements to be implemented in real-life. This manuscript investigates the major shortcomings of the existing models and offers solutions to overcome them with an improved framework and modeling method.