Moving walkways are pedestrian dedicated hardware which generally decrease pedestrian travel time. We propose the utilization of these devices to dynamically control pedestrian flows in order to improve pedestrian dynamics. Three variations of a control strategy which use moving walkways are proposed in the context of a dynamic pedestrian management system. In this paper, we discuss how a control strategy based upon either historical data, real-time data or predicted data can improve pedestrian dynamics. After analysing whether moving walkways as a control strategy are beneficial for pedestrians, focus is given towards the advantages of the predictive algorithm over the reactive (real-time) algorithm. While the reactive algorithm exploits real-time measurements, the predictive approach exploits the Adaptive Large Neighbourhood Search (ALNS) optimization algorithm to seek the optimal configuration of the moving walkways over the short term future. Furthermore, we discuss the advantage of using multi-objective optimization to explicitly quantify the trade-off between improving pedestrian travel time or congestion. The simulation results show that the predictive algorithm is generally fairer across different population groups and is better at preventing high congestion. Furthermore, the predictive algorithm can significantly decrease the travel time compared to the reactive and reference scenarios. The results from the prediction approach emphasize the trade-off which must be made between travel time and density. Finally, these results confirm the need for control strategies which are tailored to pedestrian flow dynamics.