Multi-stage dynamic programming algorithm for eco-speed control at traffic signalized intersections

Researchers have attempted to compute a fuel-optimal vehicle trajectory by receiving traffic signal phasing and timing information. This problem, however, is complex when microscopic models are used to compute the objective function. This paper suggests use of a multi-stage dynamic programming tool that not only provides outputs that are closer to optimum, but are also computationally much faster. It uses a recursive trajectory generation that is similar to least-cost path-finding algorithms that optimizes the upstream profile while comparing discretized downstream cases. Since dynamic programming is faster than traditional computational methods, the algorithm can afford to use microscopic models and thereby be sensitive to a multitude of inputs such as grade, weather etc. Agent-based simulations suggest fuel savings in the range of 19 percent and travel-time savings of 32 percent in the vicinity of intersections. This research also showed potential benefits to vehicles following a vehicle that uses the proposed logic.