Proactive traffic merging strategies for sensor-enabled cars

Congestion is a major challenge in today's road traffic. This paper addresses the issue of how to optimize traffic throughput on highways, in particular for intersections where a ramp leads onto the highway. In our work we assume that cars are equipped with sensors: they can detect the distance to the neighboring cars and communicate their velocity and acceleration among each other. We present proactive traffic control algorithms for merging different streams of sensor-enabled cars into a single stream. The main idea of a proactive merging algorithm is to decouple the decision point from the actual merging point. Sensor-enabled cars allow us to decide where and when a car merges before it arrives at the actual merging point. This leads to a significant throughput improvement for the traffic as the speed can be adjusted proactively. Sensor-enabled cars can locally exchange sensed information about the traffic and adapt their behavior much earlier than regular cars. We compare the traffic merging algorithms against a conventional priority-based merging algorithm in a controlled simulation environment. We show that proactive merging algorithms outperform the priority-based merging algorithm in terms of throughput and delay. Our experiments demonstrate that the traffic throughput can be increased by up to 200% and the delay can be reduced by 30%.