Reinforcement learning for the control of traffic flow on highways
Date
2018-12
Authors
Journal Title
Journal ISSN
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Publisher
Stellenbosch : Stellenbosch University
Abstract
ENGLISH ABSTRACT: Traffc congestion has become a significant problem around the world, not only in first-world
countries, but also in third-world countries such as South Africa. Due to spatial limitations, especially in well-developed metropolitan areas, which typically experience the worst congestion problems, capacity expansion is not always feasible for relieving the pressure on the transportation
network. Furthermore, the theory of induced traffic demand suggests that increasing highway capacity is not a long-term solution to traffic congestion due to additional traffic demand
on new or updated routes, induced by commuters' perception that new or upgraded routes should be congestion free. As a result, various approaches toward improving highway traffic
flow without increasing infrastructure capacity have been proposed in the literature.
Ramp metering and variable speed limits are the best-known control measures for effective traffic flow on highways. In most approaches towards solving the control problems presented by these control measures, optimal control techniques or online feedback control have been employed.
Feedback control does not, however, guarantee optimality with respect to the on-ramp metering
rate or the speed limit chosen, while optimal control techniques are limited to small networks
due to their large computational burden.
Reinforcement learning is a promising alternative, providing the means and framework required
to achieve near-optimal control policies at a fraction of the computational burden associated
with optimal control algorithms. In this dissertation, a decentralised reinforcement learning
approach is adopted towards simultaneously solving both the ramp metering and variable speed
limit control problems.
The dawn of the autononomous vehicle promises further improvements in traffic flow which
may be achieved over and above those of the aforementioned established highway traffic control
measures, if their capabilities are harnessed effectively. A novel method of ramp metering
by autonomous vehicles is introduced in this dissertation, based on the premise that specific
instructions may be provided to autonomus vehicles travelling along an on-ramp. The control problem presented by this method of ramp metering via autonomous vehicles is also solved using a reinforcement learning approach.
The above solution approaches are implemented as a concept demonstrator within a simple,
benchmark microscopic highway traffic simulation model. The effectiveness of the decentralised
reinforcement learning approach is evaluated by means of statistical comparisons within the context of this simple benchmark simulation model. These approaches are finally applied within the context of a real-world case study simulation model of a section of the N1 highway outbound
out of Cape Town, South Africa in order to demonstrate the effectiveness of the approaches
within the context of a realistic scenario based on a real highway network and real traffic flow data.
AFRIKAANSE OPSOMMING: Raadpleeg teks vir opsomming
AFRIKAANSE OPSOMMING: Raadpleeg teks vir opsomming
Description
Thesis (PhD)--Stellenbosch University, 2018.
Keywords
Reinforcement learning, Highway -- Traffic control, Ramp metering (Traffic engineering), Variable speed limits, Autonomous vehicles, UCTD