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Platoon-Based Self-Scheduling for Real-Time Traffic Signal Control

Xiao-Feng Xie, Gregory Barlow, Stephen Smith , and Zack Rubinstein
Proceedings 14th International IEEE Conference on Intelligent Transportation Systems, Washington DC, October, 2011., October, 2011.


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Abstract
Abstract— In this paper, we take a self-scheduling approach to solving the traffic signal control problem, where each intersection is controlled by a self-interested agent operating with a limited (fixed horizon) view of incoming traffic. Central to the approach is a representation that aggregates incoming vehicles into critical clusters, based on the non-uniformly distributed nature of road traffic flows. Starting from a recently developed signal timing strategy based on clearing anticipated queues, we propose extended real-time decision policies that also incorporate look-ahead of approaching vehicle platoons, and thus focus attention more on keeping vehicles moving than on simply clearing queues. We present simulation results that demonstrate the benefit of our approach over simple queue clearing, both in promoting the establishment of “green waves” where vehicles move through the road network without stopping and in improving overall traffic flows.

Notes
Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems
Associated Lab(s) / Group(s): Intelligent Coordination and Logistics Laboratory
Associated Project(s): Adaptive Traffic Light Signalization

Text Reference
Xiao-Feng Xie, Gregory Barlow, Stephen Smith , and Zack Rubinstein, "Platoon-Based Self-Scheduling for Real-Time Traffic Signal Control," Proceedings 14th International IEEE Conference on Intelligent Transportation Systems, Washington DC, October, 2011., October, 2011.

BibTeX Reference
@inproceedings{Xie_2011_7043,
   author = "Xiao-Feng Xie and Gregory Barlow and Stephen {Smith } and Zack Rubinstein",
   title = "Platoon-Based Self-Scheduling for Real-Time Traffic Signal Control",
   booktitle = "Proceedings 14th International IEEE Conference on Intelligent Transportation Systems, Washington DC, October, 2011.",
   month = "October",
   year = "2011",
   number= "CMU-RI-TR-",
}

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