The Optimal Route Planning for Road Network with Obstacles Using a Mixed-fleet Traffic Model

Urban traffic has faced a much greater challenge with the increase of vehicle population as society and technology advance. Modern intelligence transportation system (ITS) combines state-of-the-art communication and sensing techniques to provide a more efficient, more economic, and safer on-road environment. This research investigates the advantages of a centralized ITS management strategy with autonomous vehicle and develop a method to provide efficient traffic operation of mixed fleets when on-road obstacles are present. We use a refined cellular automata traffic model to better capture the characteristics of multi-lane mixed-fleet driving behaviors, such as lane-splitting and overtaking, in Taipei. An optimization is formulated to obtain the best obstacle avoidance strategy to maintain high traffic flow in a single road. Results show that with our method, an obstacle that usually cause serious congestion can improve the traffic flow by 20% (for 30% vehicle density) to 40% (for 60% vehicle density). We further extend the single-lane obstacle avoidance strategy to a network with multiple roads. Dijkstra algorithm is used to search for the minimal traveling time in network while each road could potentially occupied by multiple obstacles. Compared with being stocked on the original driving path, our method can suggest a new path with 60% less travel time. Our proposed method has the potential to include traffic lights or complex driving conditions for more effective and practical ITS in the future.