Past political regimes and socio-economic imbalances have led to the formation of a transport system in the Republic of South Africa (RSA) that is unique to the developing world. Affluent communities in metropolitan cities are situated close to economic activity, whereas the people in need of public transport are situated on the periphery of the cities. This demographic structure is opposite to that of developed countries and complicates both the provision of transport services and the planning process thereof. Multi-Agent Transport Simulation (MATSim) has been identified as an Agent-Based Simulation (ABS) approach that models individual travellers as autonomous entities to create large scale traffic simulations. The initial implementation of MATSim in the RSA successfully simulated private vehicle trips between home and work in the province of Gauteng, proving that there is enough data available to create a realistic multi-agent transport model. The initial implementation can be expanded to further enhance the simulation accuracy, but this requires the incorporation of additional primary and secondary activities into the initial transport demand. This study created a methodology to expand the initial implementation in the midst of limited data, and implemented this process for Gauteng. The first phase constructed a 10% synthetic population that represents the demographic structure of the actual population and identified various socio-demographic attributes that can influence an individual's travel behaviour. These attributes were assigned to the synthetic agents by following an approach that combines probabilistic sampling and rule-based models. The second phase used agents' individual attributes, and census, National Household Travel Survey (NHTS) and geospatial data to transform the synthetic population into a set of daily activity plans - one for every agent. All the agents' daily plans were combined into a plans.xml file that was used as input to MATSim, where the individuals' activity plans were executed simultaneously to model the transport decisions and behaviour of agents. Data deficiencies were overcome by contemplating various scenarios and comparing the macroscopic transport demand patterns thereof to the results of the initial implementation and to actual counting station statistics. This study successfully expanded the initial home-work-home implementation of MATSim by including additional non-work activities in the transport demand. The addition of non-work activities improved the simulation accuracy during both peak and off-peak periods, and the initial demand therefore provides an improved representation of the travel behaviour of individuals in Gauteng. / Dissertation (MEng)--University of Pretoria, 2011. / Industrial and Systems Engineering / unrestricted

Transport demand planning in South Africa is a neglected field of study, using obsolete methods to model an extremely complex, dynamic system composed of an eclectic mix of First and Third World transport technologies, infrastructure and economic participants. We identify agent-based simulation as a viable modelling paradigm capable of capturing the effects emerging from the complex interactions within the South African transport system, and proceed to implement the Multi-Agent Transport Simulation Toolkit (MATSim) for South Africa's economically important Gauteng province. This report describes the procedure followed to transform household travel survey, census and Geographic Information System (GIS) data into an activity-based transport demand description, executed on network graphs derived from GIS shape files. We investigate the influence of network resolution on solution quality and simulation time, by preparing a full network representation and a small version, containing no street-level links. Then we compare the accuracy of our data-derived transport demand with a lower bound solution. Finally the simulation is tested for repeatability and convergence. Comparisons of simulated versus actual traffic counts on important road network links during the morning and afternoon rush hour peaks show a minimum mean relative error of less than 40%. Using the same metric, the small network differs from the full representation by a maximum of 2% during the morning peak hour, but the full network requires three times as much memory to execute, and takes 5.2 times longer to perform a single iteration. Our census- and travel survey-derived demand performs significantly better than uniformly distributed random pairings of home- and work locations, which we took to be analogous to a lower bound solution. The smallest difference in corresponding mean relative error between the two cases comes to more than 50%. We introduce a new counts ratio error metric that removes the bias present in traditional counts comparison error metrics. The new metric shows that the spread (standard deviation) of counts comparison values for the random demand is twice to three times as large as that of our reference case. The simulation proves highly repeatable for different seed values of the pseudo-random number generator. An extended simulation run reveals that full systematic relaxation requires 400 iterations. Departure time histograms show how agents 'learn' to gradually load the network while still complying with activity constraints. The initial implementation has already sparked further research. Current priorities are improving activity assignment, incorporating commercial traffic and public transport, and the development and implementation of the minibus taxi para-transit mode. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Industrial and Systems Engineering / unrestricted