Travelers' preferences impact their travel choices, which can be improved through continuous learning. This study considers two types of preferences and proposes updating mechanisms for compulsive preference. By using reinforcement learning models, the researchers found that incorporating preference learning can lead to more efficient travel decisions and significantly reduce total travel time. This approach could be beneficial for managing urban traffic congestion in the future.