BeeJamA: A Distributed, Self-Adaptive Vehicle Routing Guidance Approach

We present and evaluate our distributed and self-adaptive vehicle routing guidance approach, termed BeeJamA, which provides drivers safely with routing directions well before each intersection. Our approach is based on a multiagent system, which is inspired by the honey bee foraging behavior. It relies on a distributed vehicle-to-infrastructure architecture. On the basis of microscopic traffic simulations under varying penetration rates, we show that BeeJamA outperforms dynamic shortest path algorithms with respect to average (global) travel times and regarding congestion avoidance.