Abstract: The autonomous driving method was adopted to acquire the driving data of heavy coaches in Beijing city, and the wavelet transform method used to deal with data under short stroke classification. The self-developed GPS vehicle information management system was used to monitor and collect the driving data of 400 heavy coaches equipped with driving recorders for 2 months. Then the principal components of micro trips were classified through the method of principal component analysis and K clustering technology. The Best Incremental Method and Combined Method were integrated to establish a speed change curve as representative driving cycle. Through the validation of the characteristic parameters and the joint probability distribution of velocity and acceleration, it was shown that the constructed driving cycle could comprehensively reflect the driving characteristics of heavy coaches in Beijing city. By comparison with domestic and international representative driving cycles, it was found that the constructed heavy coach’s driving cycle in Beijing city has the characteristic of having short idle time, moderate acceleration and deceleration time, and long uniform driving time, which was very different from the driving cycle of passenger car and public transport in Beijing city and European countries in traffic pattern. This is an important supplement to the research of driving cycle of heavy coaches.