In this paper, we investigate area-based pricing strategies for congested multimodal urban networks. We propose a time-dependent pricing scheme where the tolls are iteratively adjusted through a Proportional-Integral type feedback controller, based on both the level of congestion represented by the Macroscopic Fundamental Diagram, and user's adaptation to the toll cost. With this pricing scheme, we also integrate incentive schemes to improve public transport (PT) services and motivate mode shift, with the objective of increasing the efficiency of the pricing. The proposed pricing strategies are tested in the Sioux Fall network case study with an agent-based simulator and reveals appealing results: (i) the proposed toll scheme is highly effective in congestion reduction, (ii) comparing to similar toll schemes in literature the proposed pricing scheme is more flexible in toll adjustment as it takes user adaptation into account, (iii) integrating incentive programs such as PT accessibility improvement or money award for PT mode shift using the collected toll revenue, the proposed pricing scheme achieves large welfare gain, and (iv) the proposed toll scheme shows a smooth user equilibrium in long-term operation.