The Automated People Mover (APM) is an important asset for many airports to transport passengers inside or between terminal and satellite buildings An APM system normally runs on fixed schedules throughout the day, which means that the capacity of the APM is pre-determined and not depending on the actual demand. This at times can cause either an overcapacity, which leads to a waste of resources, or an under capacity, which results in passengers waiting at the station. Especially the latter factor is problematic, as it reduced passenger experience and can negatively affect the transfer process between airport facilities. In order to better match the offered APM capacity with the demand, it is proposed in this paper to use sensor-based predictive control system, which adapts the APM system capacity to real-time demand. By means of sensor data, passenger numbers are determined before they walk onto the stations platforms, and subsequently the APM system capacity is adjusted to the measured demand. In principle there are two methods to change the APM system capacity, i.e.: 1) by changing the APM capacity (i.e. more cars per train) or 2) by changing the frequency. A simulation test case was designed to provide numerical insight in the potential of adaptively changing the capacity of an APM, based on sensor derived real-time demand. The test case was derived from a variety of typical systems used worldwide and represents a complex APM system. From the simulation results it is concluded that an intelligent design of the control system results in significant improvements in terms of passenger experience, operational cost, capital cost and emission footprint. The favourable method of adjusting capacity to demand is by increase train capacity, before reducing the headway between trains.@en