The rapidly evolving aviation environment, driven by the Fourth Industrial Revolution, encompasses smart operations, communication technology, and automation. Airports are increasingly developing new autonomous innovation strategies to meet sustainability goals and address future challenges, such as shifting labor markets, working conditions, and digitalization (ACI World, 2019). This paper explores high-level governance strategies, a benchmarking study, that facilitates this transition. It aims to identify the key characteristics and features of the benchmarking study applicable to the development of autonomous airside operations. It also examines areas for improvement in operations, focusing on Key Performance Areas (KPAs) and strategic objectives related to airside automation. The findings highlight several essential performance areas and formulate it to a tailored benchmarking study that airports or aviation stakeholders can adopt to develop automation in airside operations. These criteria and features are summarized into a benchmarking framework that reflects strategy objectives. This paper contributes a valuable benchmarking methodology, supporting the growing global aviation demand for improvements toward more sustainable and smart autonomous airside operations. This outcome motivates aviation stakeholders to innovate to meet environmental and social sustainability goals.
The aim of this research/project is to investigate and analyze the opportunities and challenges of implementing AI technologies in general and in the transport and logistics sectors. Also, the potential impacts of AI at sectoral, regional, and societal scales that can be identified and chan- neled, in the field of transport and logistics sectors, are investigated. Special attention will be given to the importance and significance of AI adoption in the development of sustainable transport and logistics activities using intelligent and autonomous transport and cleaner transport modalities. The emphasis here is therefore on the pursuit of ‘zero emissions’ in transport and logistics at the urban/city and regional levels.Another goal of this study is to examine a new path for follow-up research topics related to the economic and societal impacts of AI technology and the adoption of AI systems at organizational and sectoral levels.This report is based on an exploratory/descriptive analysis and focuses mainly on the examination of existing literature and (empirical) scientific research publica- tions, previous and ongoing AI initiatives and projects (use cases), policy documents, etc., especially in the fields of transport and logistics in the Netherlands. It presents and discusses many aspects of existing challenges and opportunities that face organizations, activities, and individuals when adopting AI technology and systems.
It is expected that future transportation technologies will positively impact how passengers travel to their destinations. Europe aims to integrate air transport into the overall multimodal transport network to provide better service to passengers, while reducing travel time and making the network more resilient to disruptions. This study presents an approach that investigates these aspects by developing a simulation platform consisting of different models, allowing us to simulate the complete door-to-door trajectory of passengers. To address the future potential, we devised scenarios considering three time horizons: 2025, 2035, and 2050. The experimental design allowed us to identify potential obstacles for future travel, the impact on the system’s resilience, and how the integration of novel technology affects proxy indicators of the level of service, such as travel time or speed. In this paper, we present for the first time an innovative methodology that enables the modelling and simulation of door-to-door travel to investigate the future performance of the transport network. We apply this methodology to the case of a travel trajectory from Germany to Amsterdam considering a regional and a hub airport; it was built considering current information and informed assumptions for future horizons. Results indicate that, with the new technology, the system becomes more resilient and generally performs better, as the mean speed and travel time are improved. Furthermore, they also indicate that the performance could be further improved considering other elements such as algorithmic governance.
