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Driving cycles are usually defined by vehicle speed as a function of time and they are typically used to estimate fuel consumption and pollutant emissions. Currently, certification driving cycles are mainly used for this purpose. Since they are artificially generated, the resulting estimates and analyzes can generally be biased. In order to address these shortcomings, recent research efforts have been directed towards development of statistically representative synthetic driving cycles derived from recorded real-world data. To this end, this paper focuses on synthesis of multidimensional driving cycles using the Markov chain-based method and particularly on their validation. The synthesis is based on Markov chain of fourth order, where the road slope is accounted, as well. The corresponding transition probability matrix is implemented in the form of a sparse matrix parameterized with a rich set of recorded city bus driving cycles.