Intelligent Airport Research Articles

Flight Delay Prediction With Priority Information of Weather and Non-Weather Features

Flight delay prediction is a major topic in intelligent airport management systems, which emphasizes the use of historical data and potential features to estimate whether a future flight will delay. However, many factors affect flight delays, and these factors can be categorized into weather features (e.g., temperature, humidity, and wind speed) and non-weather features (day-of-month, day-of-week, scheduled departure and arrival time). Moreover, the impacts of weather and non-weather factors on flight delays are different. Weather features play a more important role in adverse weather conditions and are the main reason for long flight delays. When the weather condition changes from severe to non-severe, non-weather features are the main reason for flight delays, and the caused delays are relatively short. Such different impacts on flight delays raise a strong need for considering the priority information of weather and non-weather features in flight delay prediction. In this paper, we design a variant of the Random Forest model to consider the priority information of weather and non-weather features to predict flight delays. A clustering algorithm-based analysis approach is developed to assess the impact of weather and non-weather features on flight delays and draw conclusions on the priority information of weather and non-weather features. A probability sampling method is embedded in the Random Forest at the feature selection stage to perform a prior choice for weather and non-weather features to help select the key influential features. Experiments are carried out on U.S. domestic flights in July 2018, and the comparison results demonstrate that the proposed model can significantly increase flight delay prediction accuracy.

Application Problems and Solutions of Intelligent Airport Service Robots

With the development of new technologies such as artificial intelligence, big data, and cloud computing, the “intelligent airport” is considered to be an effective means to solve or alleviate the current industry problems such as large-scale airport business, the large number of operating entities, and the complicated operation conditions. This paper is about the collaboration between universities and enterprises based on the concept of service design. Relying on big data and cloud computing technology, this paper addresses the problems of airport service robots in inquiries, blind spots of security inspection, and full monomer smart navigation diffluence, combined with the basic technology of service robot artificial intelligence and the third-party interface to design solutions to effectively solve the problems of process.

Research on the Construction of Humanistic Airport

Humanistic airport is an important part of the construction objectives of “four types of airports”, namely safe airport, green airport, intelligent airport and humanistic airport. The construction of humanistic airport puts forward higher requirements for airport planning, design and operation management, emphasizing people-oriented and paying attention to passenger travel experience. The connotation, characteristics and problems in current planning, design and management of humanistic airports were analyzed. Drawing lessons from successful cases at home and abroad, this paper put forward suggestions and measures for the construction of China’s humanities airport.

Efficiency and Robustness in a Support Platform for Intelligent Airport Ground Handling

One of the effects of the development of air traffic during recent years has been the increase of congestion on major airports and their aprons, where ground handling operations take place. Efficiency and safety issues become important on the aprons. The “Integrated Airport Apron Safety Fleet Management—AAS” project aims at setting a decision support system for efficient and safe management of apron traffic, taking advantage of information gathered by “intelligent” vehicles equipped with on-board positioning and monitoring systems. In this project, we optimize the assignment of vehicles to apron operations. We present a mathematical formulation of the problem and sketch a fast sequential heuristic, which provides efficient assignments based on real-time data on position and status of vehicles and operations. We then discuss how the procedure can reach good trade-offs between efficiency and robustness against unpredicted, but frequent, delays occurring in real time at airports. The proposed approach has been applied to a major European airport (Berlin's Tegel), leading to efficiency improvements and reduction of expected delays due to handling operations.

Developing the intelligent airport

The proliferation of common-use and self-service technologies, the emergence of the mobile device as a multi-purpose tool and accompanying IT infrastructure upgrades are offering significant opportunities to enhance the airport operating environment and build a truly ‘intelligent’ airport. To benefit fully from these advances, however, it will be vital to have a cohesive and targeted technology strategy, ensuring the various systems are all well integrated. Achieving this integration will require collaboration from all stakeholders, including passengers, airlines, governments and airport authorities, on a global basis. Worldwide standards are also needed for the effective sharing and protection of data (‘business intelligence’) generated by technologies using smartphones, near-field communication, biometrics and the like. Stakeholders need to work together to exchange information and be flexible enough to respond to the varied needs of passengers, airlines and airports.

Indoor airport radio-over-fiber network traffic model and performance analysis using load-balancing techniques [Invited

The development of airports such as the recently inaugurated Heathrow Terminal 5, as well as the increasing number of people using these facilities every day for business reasons or just for pleasure, reveals the importance of adequate and reliable communication facilities in this context. This environment is characterized by multiservice, multistandard wireless technologies and a highly variable traffic demand in space and time due to passenger flow, behavior, and usage from the terminal's entrance to the corresponding boarding gate, ruled by flight departure schedules, in the indoor environment. The Intelligent Airport (TINA) project establishes its objective as the creation of a seamless hybrid wireless and wired infrastructure capable of providing users with a wide range of services, based on radio-over-fiber (RoF) networks, anticipating the deployment of multiple air interfaces within 4G networks. In this paper the spatial and temporal traffic demand is analyzed and modeled through simulation, as a means of selecting the optimum location for the base stations/antenna units (BSs/AUs) in the network. A load-balancing technique is applied to ease the load on congested cells using strategically located fixed relay nodes, and the network's behavior is analyzed for different BS properties.