In signalized urban traffic, the need for energy-efficient trajectory optimization is increasingly pressing. Conventional approaches treat signal phases as discrete passing windows; in multi-intersection corridors this hampers global optimality, yields discontinuous speed plans, and fragments control decisions. Prior work has used time windows to guide speed, but has not systematically modeled the linkage between the speed feasibility domain and vehicle-level control feasibility. We propose a signal-coupled velocity field control method that maps discrete signal phase and timing data into a continuously varying speed–position feasibility domain along the path, providing a real-time, signal-aware admissible speed range. By integrating traffic-signal constraints with vehicle dynamics and other physical limits, a composite speed upper bound is formed and used to generate reference trajectories within a nonlinear model predictive control (NMPC) framework. In a path–time formulation, longitudinal dynamic reachability is further coupled to produce a dynamic upper bound, which is embedded as a key constraint in NMPC. This tightly links signal information and vehicle dynamics in a single optimization layer. Simulation studies across multiple intersections show smoother speed profiles, markedly fewer full stops, and reductions of approximately 15%–18% in energy consumption per 100 km, together with lower safety exposure. The results demonstrate coordinated improvements in efficiency, energy use, and safety.
Zheng et al. (2026) studied this question.