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 their work the authors 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. The authors present proactive algorithms for merging different streams of sensor-enabled cars into a single stream. A proactive merging algorithm decouples the decision point from the actual merging point. Sensor-enabled cars allow them 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. The authors compare the traffic merging algorithms against a conventional priority-based merging algorithm in a controlled simulation environment. They show that proactive merging algorithms outperform the priority-based merging algorithm in terms of throughput and delay. Their experiments demonstrate that the traffic throughput can be increased by up to 200 % and the delay can be reduced by 30 %.
Proactive traffic merging strategies for sensor-enabled cars
2007
10 Seiten, 11 Bilder, 4 Tabellen, 22 Quellen
Aufsatz (Konferenz)
Englisch
Next Step: Merging Smart Cars with Driver Cars
TIBKAT | 2019
|British Library Conference Proceedings | 2008
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