D-1 Demand Alignment Predictions

European network operational stakeholders have become increasingly aware of the need to optimize their operations within the available capacity. The utilization of the available capacity on the day of operations (D0) requires the publication of a stable operational plan the day prior to operations (D-1). This D-1 plan enables all stakeholders to anticipate the network plan, and adjust their own operations accordingly.

An important part of the development of the D-1 plan is the forecasting of traffic demand. In the case of Schiphol there is a need to generate more accurate and consistent traffic predictions. However, the operational stakeholders each have a different view of the traffic demand, with different timelines for the calculation and use of the demand data. As a consequence the current D-1 planning process does not support collaborative decision making on the basis of a common view.


To70 was asked to evaluate which methods and tools the sector stakeholders use for their D-1 planning process and how the stakeholders make their choices based on this planning. In order to coordinate demand forecasts between Schiphol stakeholders, it is important to gain insight into:


  • Applied methods and sources of demand predictions;
  • The resulting differences in outcome;
  • Interpretations of traffic volume and capacity;
  • Circumstances, assumptions and restrictions that the results may affect.


The D-1 Demand Predictions Alignment report presents results of an analysis which was conducted to find the differences in the stakeholders’ D-1 process and source data, as well as differences in the resulting outcome of each of the different stakeholders’ prediction. It recommends an aligned  D-1 process based on common data. Aim of alignment is to enable all stakeholders to obtain a shared view on D-1 demand predictions to base their resource decisions on.


The following conclusions are drawn:

  1. Differences in daily LVNL and RSG activities contribute to inconsistency of stakeholder and resource decision making.
  2. Data Analysis revealed significant differences between LVNL and RSG datasets, both in terms of traffic numbers, as well as predicted timestamps.
  3. Data analysis findings clearly indicate that NM PREDICT data as used by LVNL enables a more accurate demand prediction than use of only scheduled data.
  4. RSG and airlines will significantly benefit improving accuracy when using NM PREDICT data. In case of demand reductions are required, the implementation of a local rule would ensure that sacrifices in terms of cancellations are a shared effort amongst airlines.


The following recommendations are determined:

  1. Flight matching between datasets and dates is required for further flight correlations.
  2. Continue D-1 demand prediction data analysis in order to let benchmarking conclusions be complete and representative.
  3. Validate demand predictions accuracy against such benchmark, in order to assign credit to the changes and communicate the accuracy improvement to airlines and sector stakeholders.
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