These days, aviation industry is faced by one of the toughest economic challenges of our time due to the outbreak of the new corona virus. The resulting political restrictions make it almost impossible to keep operations running in an economically profitable way especially for the air travel industry. Air traffic is restricted almost everywhere.

To face these challenges, we developed X-VIAP-Covid based on our validated Door-to-Door tools, to optimize the choices for fleet usage in current times as well as preparing an exit strategy. To cope with the pandemic challenge and the large disruption of the air transport market the underlying model system was enriched by three additional components.

Confidence to fly

SARS-CoV-2 a new, hazardous experience makes people feel insecure. Insecurity results in low confidence to travel which is part of the human behaviours in general. The underlying assumption of the model part addressing “confidence to fly” is the fear of getting infected with SARS-CoV-2. However, fears are not based on rational evaluations of the respective situations. It's rather a strong feeling of general unpleasantness regarding the specific circumstances. To approximate the factors which are relevant contributors in constituting this fear, psychological research results of general loss aversion of humans were used. In principle humans try to avoid any course of action, which makes their situation worse that their current one. With regards to air travel, we modelled this kind of behavioural tendency with the comparison of the origins and destinations SARS-CoV-2 situation. If the situation in the destination is worse than in the origin, the confidence to travel will be lowered significantly. Factors used concern the medical treatment in a foreign country, the health system, to be forced into quarantine, likelihood to have Covid-19 on board of the flight, and other local policy measures preventing the spread of the virus up to the risk that there is no possibility to return home due to flights are suspended and borders are closed. To embed the psychological certainty effect non-linear formulations of the models were applied.

Mobility change

Even though the world around us is constantly changing, people are resistant to change. More often than not people stay in their comfort zone, take the default option when they don't know any better or do things the way they have always been done. Changes from these patterns are triggered, when we are forced to leave said comfort zone and do things differently than before. Crises trigger such changes and the pandemic triggers security, disease and the economic effects on a worldwide dimension resulting into less air travel. On top there is the immanent bounce back effect of the virus. Especially the latter one forces everyone to adjust priorities and to adjust the comfortable daily life to fast changing circumstances either caused by the health situation or political measures imposed. The behavioural change concerns a massive change on working level by using virtual meetings and home office, vacation trips were redirected to local activities or short distance destinations and VFR traffic were on hold wherever possible. Everyone was urged to leave the comfort zone and aviation faces a cut in business travel and a time lag for VFR and vacation demand. On top airlines and airports are forced to follow new guidelines and policy measures are imposed pricing CO2 emissions. The circumstances addressed are translated into technical, economical and network constraints as well as regional competition reflected by model variables used on the demand side and the characteristics of the travel paths.

Pandemic development

The spread of the virus was put into relationship to air, surface and community activities / mobility whereby policy measures in place were considered. This allows also examining the future development of the Covid-19 situation to foresee policy actions and the necessities to adjust air services in capacity and destination. This model component does neither substitute the pandemic models nor the medical forecasts resulting from them. However, some of their mathematics, values and indicators are of great value to explain relationships and to trigger the generation process of an air network as an adaptive strategy.

Due to the fast changing environment of the pandemic situation, evoking policy measures and competitive reactions the tool allows weekly forecasts, which can be cumulated at least to a periodically four-week-forecast to satisfy operational needs. For mid- and long-term forecasts we recommend to use our SONARNetGen model which incorporates a Hammer & Dance process. This process reflects local policies (the Hammer) upon the pandemic development and airlines reaction adjusting air services in capacity and destination (the Dance).

The model components of X-VIAP dealing with airport/route choice, intra- and inter-modality etc. remained in place so that the reflection of the competitive situation is ensured. Due to SARS-CoV-2 some specific analyses were added and some existing were enriched to foresee demand effects, e.g. thresholds of new infections per 100.000 inhabitants, demand at risk in the route specific catchment, likelihood of infected passengers on board.