Speed-Guidance Model for Connected and Autonomous Bus Based on Dynamic Platooning

To address the speed-guidance problem of connected and autonomous buses (CABs) traversing a corridor of signalized intersections, proposing a three-stage speed-guidance framework: “dynamic platooning, dynamic guidance, and conflict feedback”. Firstly, the control zone is partitioned into a dynamic-platooning zone, a dynamic-guidance zone, and a conflict zone. Characterize the heterogeneous dynamic features of Human-Driven Vehicles (HDVs), Connected and Autonomous Vehicles (CAVs), and CABs using the car-following model. Secondly, with the ‘maximum number of vehicles passing through’ un-der fixed signal timing as the global optimization objective, derive the target vehicle speed, update the time window and vehicle grouping, and achieve different optimization goals in different zones. Finally, the intersection group around the Beijing Olympic Park is selected as the research scenario, and experiments are conducted using the SUMO-MATLAB co-simulation platform. Results show that, compared with no guidance and the classical static-guidance model, the proposed model reduces the average number of stops by up to 77.56%, cuts the average delay by up to 8 s, raises the average speed by up to 1.5 m/s, and lowers fuel consumption per kilometer by up to 70.13%.