Because static pricing models (such as flat-rate or tiered-rate models) cannot improve user utility for subscribers and ease network congestion for operators during peak time, Smart Data Pricing has become an important incentive for mobile data markets. Paris Metro Pricing (PMP), which is a static pricing mode inspired by the pricing model for the Paris metro system, uses differentiated prices to motivate users to choose different train classes. Before choosing a class, people will consider their expected quality of service versus the prices that they are willing to pay. Even though PMP cannot guarantee the actual quality of service during service time, a balance between users' utilities and operators' revenue is achieved. In this paper, we propose an adaptive PMP scheme, so-called APMP, which determines the dynamic access prices of different classes for the next 24 h. The accessible prices should try to increase the revenue while operators can serve more subscribers. Our simulation results show that APMP can significantly improve total revenue and average revenue per user for the operator.