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A model for predicting travel times by mining spatio-temporal data acquired from vehicles equipped with Global Positioning System (GPS) receivers in urban traffic networks is presented. The proposed model, which uses k-nearest neighbour (kNN) non-parametric regression, is compared with models that use historical averages and the seasonal autoregressive integrated moving average (ARIMA) model. The main contribution is provision of a methodology for mining GPS data that involves examining areas that cannot be covered with conventional fixed sensors. The work confirms that the method that predicts traffic conditions most accurately on motorways and highways (namely seasonal ARIMA) is not optimal for travel time prediction in the context of GPS data from urban travel networks. In all the examined cases, kNN approach yields a mean absolute percentage error that is twice as good as ARIMA, while in some cases it even yields a mean absolute percentage error that is an order of magnitude better. The merit of the model is demonstrated using GPS data collected by vehicles travelling through the road network of the city of Zagreb. To evaluate the performance, the models mean absolute percentage error, mean error, and root mean square error are calculated. A non-parametric ranked Friedman ANOVA to test groups of three or more models, and the Wilcoxon matched pairs test to test significance between two models are used. The alpha levels are adjusted using the Bonferroni correction. Today’s commercial fastest-route guidance systems can readily incorporate the proposed model. Since the model yields travel times that are dependent on dynamic factors, these commercial systems can be made dynamic. Furthermore, the model can also be used to generate pre-trip information that will help users to save time. KEYWORDS: travel time prediction, urban traffic, GPS data, k-nearest neighbour, seasonal ARIMA, non-parametric regression