Driving cycle is a crucial topic for the auto industry. It is developed to provide a quantitative measure on the fuel consumption and emission of a vehicle. In recent years, massive amount of driving data has been collected but has not yet been commonly used for the evaluation of driving cycle. We believe the collection of such data and the advancement in analytics models may provide a fresh perspective for the construction of driving cycle. Therefore, we propose a novel clustering-based optimization method for the construction of driving cycles. We employ the principal component analysis and spectral clustering algorithms to eliminate redundant features and analyze data structure. We further develop an adaptive optimization algorithm to select the appropriate kinematic segments to form a representative driving cycle. To demonstrate the effectiveness of our method, we compare our performance against the baselines including the New European Driving Cycle (NEDC), Federal Test Procedure (FTP), and Markov chain-based methods. The model performance is evaluated with real driving data from two cities in Fujian, China. Our proposed method is shown to be superior to all baselines. In addition, based on our optimized driving cycle, we can also estimate the fuel consumption to evaluate its energy economy. To sum up, this study offers a novel methodology to establish the driving cycle based on real and localized traffic data, where the constructed driving cycle can further be used for the development of energy economy and emission control.