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. A wide collection of statistical features, including the frequency domain indicators, unique cross-correlation velocity-acceleration-slope indicators, and indicators related to bus stops at stations are considered for the purpose of driving cycle validation. To prove the synthesis method validity, a comparative statistical analysis of distributions of the nominated statistical features of synthetic and recorded driving cycles is carried out. Finally, a multi-criteria method of driving cycle validation based on lumped metrics is outlined and examined.
