Service design is literally the design of services. Service designers improve existing services or design completely new ones. Nothing new so far. Services have been around for centuries, and every service was conceived and designed by someone. However, service design takes a different angle; a different perspective as its starting point: it is a process of creative inquiry aimed at the experiences of the individual user. ‘Service design, insights from 9 case studies’ is the final publication of the Innovation in Services programme. During this programme, creative design agencies applied the methods of service design in nine different projects.
Amsterdam Airport Schiphol has faced capacity constraints, particularly during peak periods. At the security screening checkpoint, this is due to the growing number of passengers and a shortage of security staff. To improve operating performance, there is a need to integrate newer technologies that improve passing times. This research presents a discrete event simulation (DES) model for the inclusion of a shoe scanner at the security screening checkpoint at Amsterdam Airport Schiphol. Simulation is a frequently used method to assess the influence of process changes, which, however, has not been applied for the inclusion of shoe scanners in airport security screenings yet. The simulation model can be used to assess the implementation and potential benefits of an optical shoe scanner, which is expected to lead to significant improvements in passenger throughput and a decrease in the time a passenger spends during the security screening, which could lead to improved passenger satisfaction. By leveraging DES as a tool for analysis, this study provides valuable insights for airport authorities and stakeholders aiming to optimize security screening operations and enhance passenger satisfaction.
Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
