Sukki Lee

Phantom congestion in highway tunnels reduces operational efficiency and destabilizes traffic flow. In this study, the effects of a pacemaker system (PMS) on traffic operation in the Geumnam Tunnel on the Seoul–Yangyang Expressway were evaluated using a before–after analysis based on long-term vehicle detection system (VDS) data. Unlike past studies, this study provides an integrated empirical evaluation by jointly examining changes in spatiotemporal traffic flow, traffic capacity, and speed improvement at different level of service. The analyses were conducted using data from five VDS detectors installed upstream and downstream from the tunnel. After PMS installation, (i) increased average and 25th-percentile speeds at most detector locations and decreased speed standard deviation were observed near the tunnel exit and downstream sections, (ii) the maximum traffic volume increased from 1661 to 1765 veh/h/lane, and (iii) the mean speed and 25th-percentile speed increased by 6.5%, indicating speed-reduction alleviation among low-speed vehicles. Thus, the PMS increases vehicle speed, reduces speed variability, and enhances traffic flow stability and processing capability. These findings provide empirical evidence for the operational effectiveness of a PMS as a practical tool for mitigating phantom congestion in highway tunnel sections, reducing speed differences between vehicles, and improving traffic stream stability.

In this study, the traffic operational effects of a pacemaker system (PMS) on the traffic operation in the Geumnam Tunnel on the Seoul–Yangyang Expressway was evaluated herein using a before–after analysis based on long-term vehicle detection system (VDS) data. Changes in spatiotemporal traffic flow and traffic capacity, and speed improvement under different levels of service (LOS) were analyzed using data from five VDS detectors installed upstream and downstream of the tunnel. After PMS installation, (i) increased average and 25th-percentile speeds at most detector locations and decreased standard deviation of speed were observed both near the tunnel exit and the downstream sections, (ii) maximum traffic volume was increased from 1661 to 1765 veh/h/lane (~6.3% increase), (iii) LOS-based speed improvement analysis showed that mean speed and 25th-percentile speed increased by ~6.5%, indicating the alleviation of speed reduction among low-speed vehicles due to PMS. These results prove that PMS increases vehicle speed, reduces speed variability, and enhances traffic flow stability and processing capability. These findings provide empirical evidence supporting the operational effectiveness of a PMS as a practical tool for mitigating phantom congestion in highway tunnel sections and reducing the speed differences between vehicles and improve traffic stream stability.

The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS’s recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles.

Autonomous vehicle technology, employing image sensors for road marking recognition, is largely dependent on the Lane Departure Warning System (LDWS) to prevent vehicular deviations from driving lanes. The effectiveness of image-based lane recognition, however, is susceptible to environmental influences like weather conditions and the surrounding road environment. This study investigates the impact of road marking retro-reflectivity on LDWS under varied environmental conditions. Conducted at the Yeoncheon SOC Demonstration Research Center, the experiments encompassed a spectrum of weather scenarios, incorporating rainfall and shifts between day and night lighting. Controlled wear was applied to white, yellow, and blue road markings, followed by measuring their retro-reflectivity at different stages of degradation. The LDWS's performance was evaluated by assessing the recognition rates of these markings under these diverse environmental settings. The study reveals that enhanced retro-reflectivity significantly boosts LDWS detection, especially in challenging weather conditions. Furthermore, the research led to the development of a simulation framework to analyze the cost-effectiveness of road marking maintenance, aligning it with the safety needs of autonomous vehicles. This framework suggests the necessity for updated road marking guidelines that cater to the advanced needs of driver assistance systems and autonomous vehicle technology.