Since the development of the Macroscopic Fundamental Diagram (MFD), many traffic control strategies and algorigthms have been developed to implement perimeter control over a specific urban region. In most cases, the typical assumption is to have a common average trip length for all vehicles traveling within the same region. However, the proper calibration of the trip length has been proved to be of crucial importance for ensuring accurate traffic predictions from MFD models. In this paper, we propose to investigate how the calibration of these travel distances influences the traffic dynamics and the performance of a perimeter control strategy using a Model Predictive Control (MPC) scheme. In particular, we quantify the effects of an improper estimation of the travel distances inMPC applications. We also propose a methodological framework based on the Unsected Kalmann Filter (UKF), to correct for these improper estimations of trip lengths and improve the efficiency of the controller. We perform the simulation tests using an accumulation based MFD model on a medium-sized real city network.