网联混行下城市干道动态公交专用道管控策略

The Connected and Automated Vehicles (CAVs) can acquire rea l-time information on the driving states of surrounding vehicles, enabling them to make more efficient use of roadway infrastructure while ensuring priority passage for buses. This study analyzes the car-following patterns in mixed traffic flow and calibrates model parameters using the Waymo dataset. A CAV lane-changing model incorporating risk perception is then proposed. Building upon this model, a mixed traffic flow simulation framework for urban arterial roads is developed to examine the operational efficiency and risk characteristics of mixed traffic under dynamic bus lane scenarios. The adaptability of lane management strategies is evaluated in consideration of CAV penetration rates, general traffic demand, and bus stop locations. The results show that under the risk-aware intelligent lane scenario, bus travel time increases by 4.8%, while the average travel time per person decreases by 23.1%, and the number of CAV lane changes is reduced by 50.8%, compared to the non-risk-aware scenario. The intelligent lane management strategy achieves optimal performance when the CAV penetration rate is within [0.5, 0.8) and general traffic demand is within [600, 900] passenger car unit per hour with bus stops located between [50, 250] meters, or when the CAV penetration rate is within [0.8, 1.0] and traffic demand is within (900, 1 400] passenger car unit per hour with bus stops between [50, 450] meters.