Design and implementation of an NMPC-based green light optimal speed advisory for buses: A simulation platform

• A Nonlinear Model Predictive Control (NMPC) strategy is developed for Green Light Optimal Speed Advisory (GLOSA) tailored to urban buses. • The control algorithm integrates real SPaT data, bus stop dwell times, and service schedule adherence within a unified optimal control framework. • The NMPC approach achieves 15% lower energy consumption and 30% reduction in longitudinal acceleration RMS compared to a rule-based strategy, without increasing travel time. • Real-time feasibility is demonstrated through deployment as a ROS C++ node, achieving computation times well below the 1 s NMPC update interval. Green Light Optimal Speed Advisory (GLOSA) systems are a key innovation in Intelligent Transportation Systems (ITS), aiming to optimise vehicle speed profiles while harmonising with traffic light schedules. This paper presents a GLOSA system based on Non-linear Model Predictive Control (NMPC). The proposed system uses real traffic light data and accounts for bus stop dwell times to provide optimal speed alerts. The system has been assessed using a realistic simulation scenario built upon real-world data collected in Milan and implemented within IPG TruckMaker environment. Simulation results demonstrate the effectiveness of the proposed approach compared to a previously developed rule-based algorithm, as it enables the prediction of the vehicle’s future states while ensuring intersection crossings during green light phases. Moreover, real-time feasibility has been verified through deployment as a standalone ROS C++ node, achieving computational times of around 60 ms, thereby providing a new solution within the NMPC update interval of 1 s.