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To establish a representative driving cycle for public urban buses in Fuzhou city, real-world driving data of 18 buses' routes was gathered. Based on acquisition of the real operational data, about 2.2 million seconds of valid data was divided into 8262 micro trips and an evaluation metric comprised of 15 characteristic parameters was established. Next, principal component analysis was applied for dimensionality reduction of the characteristic parameters and all micro trips were classified into different clusters by K-means clustering. Subsequently, the Silhouette equation was introduced as part of the cluster selection procedure. Finally, the target driving cycle, containing a 1227 second speed-time series, was developed according to a comprehensive estimation of six characteristic parameters and a maximum of the speed-acceleration frequency distribution. The experimental results verified the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]