Evaluating the effectiveness of active vehicle safety systems

Area of Science:

• Traffic Engineering
• Automotive Safety
• Intelligent Transportation Systems

Background:

• Advanced vehicle safety systems (AVSSs) are increasingly adopted but their impact on overall traffic flow remains understudied.
• Current AVSSs primarily assist individual vehicles, with limited research on their collective effect on traffic stream dynamics.
• Understanding AVSSs' influence on crash reduction and congestion mitigation is crucial for optimizing transportation networks.

Purpose of the Study:

• To propose and evaluate a methodology for assessing the effectiveness of active vehicle safety systems (AVSSs) on traffic streams.
• To quantify the impact of AVSSs on crash potential reduction and traffic congestion mitigation.
• To analyze the influence of traffic conditions and market penetration rates on AVSS performance.

Main Methods:

• Developed a methodology using microscopic traffic simulation (VISSIM) and its COM-interface to implement AVSSs.
• Utilized a Surrogate Safety Assessment Model (SSAM) to derive indirect safety measures for effectiveness evaluation.
• Conducted simulation experiments on a 16.7-km freeway segment under varying traffic conditions (LOS D, A-C) and incident scenarios.

Main Results:

• Under LOS D conditions, AVSSs reduced rear-end and lane-change conflicts by 78.8% and 17.3%, respectively, and decreased average delay by 55.5%.
• System effectiveness was diminished under lighter traffic conditions (LOS A-C).
• During incident conditions with 100% market penetration, AVSSs reduced rear-end conflicts by 9.7% and vehicle delays by 43.9%.

Conclusions:

• AVSSs demonstrate significant potential for enhancing traffic safety and reducing congestion, particularly in dense traffic scenarios.
• The effectiveness of AVSSs is contingent upon traffic conditions and market penetration rates.
• Optimizing AVSS deployment requires intelligent management strategies that integrate traffic conditions and market penetration for maximum impact on traffic streams.