Satellite positioning measurements are never perfectly unbiased. Due to multiple types of errors affecting the signal transmission through an open space and urban areas each positioning measurement contains certain degree of uncertainty. Satellite signal receivers also do not receive the signal continuously, but the localization information is received discretely. Sampling rate and positioning error provide uncertainty towards the various positioning algorithms used in localization, logistics and in intelligent transport systems applications. This thesis examines the effect of positioning error and sampling rate on geometric and topological map matching algorithms and on the precision of route tracking within these algorithms. Also the effects of the different network density on the performance of the algorithms are evaluated. It also creates the platform for simulation and evaluation of map matching algorithms. Map matching is the process of attaching the initial positioning measurement to the network. A number of authors presented their algorithms during past decades, which shows how complex topic the map matching is, mostly due to the changing environmental and network conditions. Geometric and topological map matching algorithms are chosen, modelled and simulated and their response to the different input combinations is evaluated. Also the recommendations for possible ITS applications are carried out in terms of proposed requirements of the receiver. The results confirm general expectation that the map matching overall improves the initial position error and that map matching serves as a form of error mitigation. Also the correlation between the increase of the original positioning error and the increase of the map matching error is universal for all the algorithms in the thesis. But the comparison of the algorithm also showed large differences between the topological and geometric algorithms and their ability to cope with distorted input data. Whereas topological algorithms were clearly performing better in scenarios with smaller initial error and smaller sampling rate, geometric matching proves to be more effective in heavily distorted or very sparsely sampled data set. That is caused mostly by the ability to easily leave the wrongly mapped position which is in these situations comparative advantage of simple geometric algorithms. Following work should concentrate on involving even more algorithms into the comparison, which would produce more valuable results. Also the simulation  of the errors using the error magnitude simulation with known an improved error modelling could increase the generalization of the results.