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.　©　2018,　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.