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Fatigue is an inevitable hazard in the provision of air traffic services and it has the potential to degrade human performance leading to occurrences. The International Civil Aviation Organization (ICAO) requires air navigation services which providers establish fatigue risk management systems (FRMS) based on scientific principles for the purpose of managing fatigue. To develop effective FRMSs, it is important to investigate the relationship between traffic volume, air traffic management occurrences, and fatigue. Fifty‐seven qualified ATCOs from a European Air Navigation Services provider participated in this research by providing data indicating their alertness levels over the course of a 24‐hour period. ATCOs’ fatigue data were compared against the total of 153 occurrences and 962,328 air traffic volumes from the Eurocontrol TOKAI incident database in 2019. The result demonstrated that ATCO fatigue levels are not the main contributory factor associated with air traffic management occurrences, although fatigue did impact ATCOs’ performance. High traffic volume increases ATCO cognitive task load that can surpass available attention resources leading to occurrences. Furthermore, human resilience drives ATCOs to maintain operational safety though they suffer from circadian fatigue. Consequently, FRMS appropriately implemented can be used to mitigate the effects of fatigue. First‐line countermeasure strategies should focus on enough rest breaks and roster schedule optimization; secondary strategies should focus on monitoring ATCOs’ task loads that may induce fatigue. It is vital to consider traffic volume and ATCOs’ alertness levels when implementing effective fatigue risk management protocols.

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Although time constraints on travel behavior have been widely recognized, little effort has been made to incorporate such constraints into the traditional stochastic user equilibrium (SUE) framework. The major objective of this research is to fill this gap by incorporating travel time constraints into the SUE model by means of a nonlinear perceived travel time function. This modified model, designated the travel time budget model, focuses primarily on discretionary travel behavior (such as shopping trips) and hence also allows the possibility of deferring travel decisions by incorporating an additional choice alternative designated the shop-less-frequently alternative. This model is compared with the traditional SUE model by using a simulated travel scenario on a test network designed to reflect a practical planning situation. The simulation shows that when attractiveness levels are increased by the introduction of a new shopping opportunity, the presence of travel time constraints can lead to significantly smaller predicted travel volumes than those of the traditional SUE model. More important, it shows that the overall pattern of travel can be quite different. In particular, travel to the shopping destination with enhanced attractiveness can actually decrease for some origin locations. The findings suggest that when an attempt is made to evaluate the impact of planning alternatives on future traffic patterns, it is vital to consider not only the cost of time itself but also the time trade-offs between travel and other human activities.

No abstract