Based on traditional characteristics, the endpoint detection method of driving acoustic signals has some problems, such as low recognition rate of overlapping vehicle segment and difficulty in determining double threshold value. For the two problems, mel frequency cepstral coefficients (MFCC) cepstrum distance and short-term energy were integrated to detect traffic. First, a dual-lane road with relatively quiet environment was selected, and the driving acoustic signals containing overlapping vehicle segments were collected from the road. Second, the short-term energy and MFCC cepstrum distance were extracted. Their advantages and disadvantages of endpoint detection were analyzed and compared. Third, a new feature which integrates the short-term energy and MFCC cepstrum distance was proposed. Based on the new feature, the traditional dual-threshold decision was improved to single-threshold decision. Finally, the new feature was used to detect the vehicle segment endpoints and to count the traffic volume. The experimental results show that the endpoint detection method based on integrated feature can effectively solve the problems of low recognition rate and difficult determination of double threshold.