Collision risk analysis in the merging area of interchanges using Monte Carlo traffic simulation

Area of Science:

• Traffic Engineering
• Transportation Safety
• Computational Simulation

Background:

• Traffic simulation is crucial for evaluating interchange safety and efficiency.
• Existing research often prioritizes traffic efficiency over simulation precision and collision risk assessment under uncertainty.
• Interchange merging areas present unique challenges due to traffic flow uncertainty.

Purpose of the Study:

• To assess potential collision risk in interchange merging areas by considering traffic flow uncertainty.
• To develop a simulation method for high-fidelity traffic situations accounting for parameter uncertainty.
• To identify key risk variables impacting vehicle safety in merging zones.

Main Methods:

• Utilized Monte Carlo method for random traffic simulation of high-field traffic flow.
• Proposed Safe Braking Deceleration (SBD) as a safety index to quantify collision risk.
• Extracted risk variables: relative distance, relative speed, and merging angular velocity (MAV).

Main Results:

• Relative speed and distance had minimal impact on SBD.
• Merging Angular Velocity (MAV) significantly indicated collision risk.
• Safe Braking Deceleration (SBD) increased with rising MAV.

Conclusions:

• Developed a novel traffic simulation platform addressing parameter uncertainty for high-fidelity scenarios.
• MAV is an effective indicator of SBD and a significant factor in identifying interchange merging collision risks.
• Findings enable the design of more reliable ramp merging control strategies.