Abstract:

To address the huge data analysis problem in the decision-making for long-term road performance maintenance, this paper introduces the deep deterministic policy gradient (DDPG) reinforcement learning model in the maintenance decision analysis. A set of scientific and effective decision-making methods for long-term performance maintenance of asphalt pavements has been established through machine learning. These methods can improve road performance and make effective use of maintenance funds. Compared with the deep Q-learning network and Q-Learning algorithms, the DDPG algorithm requires less sampling data, converges faster, performs better, and can effectively improve the evaluation efficiency of the road service performance. Therefore, the proposed model plays an important role in the development of multi-objective maintenance decision-making for asphalt pavements.

Key words: traffic engineering, asphalt pavement, maintenance decision, reinforcement learning, deep deterministic policy gradient model