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Title:Real-Time Traffic Management to Maximize Throughput of Automated Vehicles
Authors:Tam Chantem and Divya Desiraju
University:Utah State University
Publication Date:Mar 2015
Report #:MPC-15-283
Project #:MPC-433
TRID #:01560894
Keywords:intelligent transportation systems, intelligent vehicles, traffic management



In intelligent transportation systems, most of the research work has focused on lane change assistant systems. No existing work considers minimizing the disruption of traffic flow by maximizing the number of lane changes while eliminating the collisions. In this thesis, we develop qualitative and quantitative approaches for minimizing the disruption of traffic flow for three lane scenarios and show that we can extend our approach to an arbitrary number of lanes. The proposed algorithm is able to achieve the maximum number of lane changes. Simulation results show that our approach provides much better performance when compared with different lane change algorithms without incurring large overhead, and is hence suitable for online use.

How to Cite

Chantem, Tam, and Divya Desiraju. Real-Time Traffic Management to Maximize Throughput of Automated Vehicles, MPC-15-283. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2015.

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