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Study of adaptation parameters of merging zones for freeway interchange ramps considering CAV & AV mixed traffic
Shibo Zhang1,2, Yun Yang1, Li Wang3
1School of Automotive and Transportation, Xihua University, Chengdu, China.
Objective:
This study aims to investigate the adaptability of existing freeway interchange ramp merging zone geometries to mixed traffic flows comprising connected automated vehicles (CAVs), autonomous vehicles (AVs), and human-driven vehicles (HDVs). It seeks to identify optimal road geometric parameters to enhance traffic safety and efficiency in such environments.
Methods:
Using a merging zone on a Chengdu expressway interchange as a case study, a VISSIM microsimulation model was constructed for a three-lane mainline with a single-ramp parallel merging zone. The study incorporated the length of forbidden lane-changing markings as a key road parameter. An orthogonal experimental design was employed to structure simulation experiments. A weighted grey clustering model assessed the safety level of the merging zone, while the entropy-weighted TOPSIS method comprehensively evaluated the merits of road parameter combinations to determine the optimal geometric design.
Results:
The results indicate that deploying CAVs can moderately improve safety in merging zones. The study also proposes the following recommended values for road parameters under mixed traffic flow conditions: a sight triangle angle of 7°; and for low CAV permeability, an acceleration lane length of 290 m, a merge taper length of 100 m, and forbidden lane-changing marking of 41 m; for high CAV permeability, these values become 275, 110, and 37 m, respectively.
Conclusions:
Under mixed traffic flow conditions involving HDVs, AVs, and CAVs, the existing geometric characteristics and traffic flow requirements of merging zones differ to some extent from conventional conditions. The parameter optimization guidelines proposed in this study can inform the design optimization and adaptive retrofitting of freeway merging zones in comparable mixed traffic environments.
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