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An evaluation of online reinforcement learning neuro-fuzzy traffic signal controllers

University of British Columbia

The work related to the development and evaluation of an online reinforcement learning neuro-fuzzy traffic signal controller undertaken at the University of British Columbia has been presented in this thesis. The main objective of the initiative was to advance the functionality of an earlier design developed at the same research facility which has been presented in the undergraduate thesis of Denesh Pohar. The original code has now been modified to control traffic movements at a standard four-leg arterial intersection. Its robustness was tested considering a range of traffic volume scenarios on the intersecting roads and simulated over a 90-minute period in Vissim traffic simulation software. Furthermore, the performance was compared to the operations of two existing signal control strategies, the actuated and modified FUSICO controls, which were also simulated under an identical set of conditions. Results suggest some positive changes in the intersection performance with the implementation of the online RLFNN control.

Text

2010-01-08

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http://hdl.handle.net/2429/17844

Civil Engineering

University of British Columbia

UBCV

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