A driving cycle construction methodology combining k-means clustering and Markov model for urban mixed roads

With the rapid urban expansion in China, there is considerable development of road networks, mainly comprising arterial roads, expressways, and loop-lines in urban regions. In addition to typical short trips, certain number of long trips are also included in the driving behavior for urban areas. In view of the new characteristics of urban mixed roads, a driving cycle construction methodology combining k-means clustering and Markov model method is proposed based on about 2.4 million seconds of driving data collected from seven passenger cars without route planning. In order to cluster micro trips into appropriate categories corresponding to different types of roads, the Silhouette equation is introduced to determine the appropriate clustering strategy and the error of artificial factors is restrained. The constructed driving cycle, comprising a 1200 s speed-time series, is developed based on the assessment of speed–acceleration frequency distribution and estimation of six characteristic parameters. According to the results, the average difference rate of six characteristic parameters is only 4.31%, which demonstrates the effectiveness of the proposed method.