Browsing by Author "Schmidt-Dumont, T."
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- ItemA case for the adoption of decentralised reinforcement learning for the control of traffic flow on South African highways(South African Institution of Civil Engineering, 2019) Schmidt-Dumont, T.; Van Vuuren, J. H.ENGLISH ABSTRACT: As an alternative to capacity expansion, various dynamic highway traffic control measures have been introduced. Ramp metering and variable speed limits are often considered to be effective dynamic highway control measures. Typically, these control measures have been employed in conjunction with either optimal control methods or online feedback control. One shortcoming of feedback control is that it provides no guarantee of optimality with respect to the chosen metering rate or speed limit. Optimal control approaches, on the other hand, are limited in respect of their applicability to large traffic networks due to their significant computational expense. Reinforcement learning is an alternative solution approach, in which an agent learns a near-optimal control strategy in an online manner, with a smaller computational overhead than those of optimal control approaches. In this paper an empirical case is made for the adoption of a decentralised reinforcement learning approach towards solving the control problems posed by both ramp metering and variable speed limits simultaneously, and in an online manner. The effectiveness of this approach is evaluated in the context of a microscopic traffic simulation model of a section of the N1 national highway outbound from Cape Town in South Africa's Western Cape Province.
- ItemOptimisation of radio transmitter locations in mobile telecommunication networks(Southern African Institute for Industrial Engineering, 2016) Schmidt-Dumont, T.; Van Vuuren, J. H.ENGLISH ABSTRACT: Multiple factors have to be taken into account when mobile telecommunication network providers make decisions about radio transmitter placement. Generally, area coverage and the average signal level provided are of prime importance in these decisions. These criteria give rise to a bi-objective problem of facility location, with the goal of achieving an acceptable trade-off between maximising the total area coverage and maximising the average signal level provided to the demand region by a network of radio transmitters. This paper establishes a mathematical modelling framework, based on these two placement criteria, for evaluating the effectiveness of a given set of radio transmitter locations. In the framework, coverage is measured according to the degree of obstruction of the so-called ‘Fresnel zone’ that is formed between handset and base station, while signal strength is modelled taking radio wave propagation loss into account. This framework is used to formulate a novel bi-objective facility location model that may form the basis for decision support aimed at identifying high-quality transmitter location trade-off solutions for mobile telecommunication network providers. But it may also find application in various other contexts (such as radar, watchtower, or surveillance camera placement optimisation).