The goal of this study is to develop a working code that performs the traffic assignment step of the classical
four step model of traffic forecasting within transport demand modelling. The traffic assignment code is to
be designed for use in an existing Genetic Algorithm (GA) developed by Nnene (2014) that optimizes the
operational frequency of public transport operations in the City of Cape Town.

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The two most prevalent methods for assigning traffic to a network are: the simplistic all-or-nothing (AON) assignment and the equilibrium assignment. All-or-nothing assignments assign all traffic to the shortest paths
between an origin and a destination without taking into account the effect of congestion. Equilibrium models
take into account the effect of congestion in transport networks by making use of heuristics that iteratively
complete a number of all-or-nothing assignments until convergence criteria - describing a transport network
equilibrium - are met.

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In this study, two all-or-nothing models are developed using the Python coding language. Both all-or-
nothing models are subsequently extended to an equilibrium assignment model that is based upon the
method of successive averages (MSA) heuristic. Both models are then tested on three hypothetical networks as a means of ascertaining the accuracy of the models. The models are then tested on the City of Cape Town
bus network as a means of ascertaining the feasibility of using the equilibrium model developed to improve
upon a genetic algorithm that optimises the operational frequency of the City of Cape Town bus network.

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