Erik Grunewald

Optimizing intermodal traffic management involving airports and railway using forecast and what-if capability

Forecasting and what-if capabilities are common to modern airport management systems. Using such a system often requires the operator to have a clear idea of how the optimal solution will look like. In this paper we consider the case of delayed trains to the airport. The question is whether and to what extend to delay flights to wait for passengers who took the delayed trains. We present an approach for assessing possible actions based on gain and loss. The definitions of gain and loss are based on the specific flight carta of affected flights and costs for delaying a flight. The methodology is based on assessing a large number of what-if runs. Costs are included as parameters and can be customized by operators.

A Neural Network Approach to Airport Management

An adaptive method for Performance Based Airport Management using a Neural Network for evaluation of the change of KPIs and appropriate action is presented. It assists the operators by evaluating past, current, and expected values of the KPIs and identifying the most promising reaction, if it is required. A first implementation of this concept covers the control of border control checkpoints which are crucial for airports serving as a hub. Based on the KPI “boarding quota“ a Neural Network is designed making suggestions about the number of open checkpoints, if there exist delays of the arriving flights. It is trained by a posteriori optimized configurations using a Genetic Algorithm.