Using Reinforcement Learning to Combine Green Light Optimised Speed Advisory or Equivilent Systems and Adaptive Traffic Lights

点击翻译按钮获取中文摘要

With the number of road users increasing and road planning and building being expensive, time consuming andenvironmentally damaging, many institutions are looking towards increasingly smarter infrastructure like new adaptivetraffic control systems and green light optimisation speed advisory systems. However, using current adaptive trafficcontrol systems green light optimisation speed advisory systems leads to inefficiency as accurate future signal plans arenot always available leading to inaccurate speed advisories. The aim of this thesis will be to employ techniques fromthe eld of machine learning to train an AI to control future signal plans as well as decide on the details to send todrivers. This thesis details the building of test bed that has been built in Python and used to demonstrate theadvantages of each system as well as the inefficiencies of using both.