Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

A Collaborative Optimization Model for Ground Taxi Based on Aircraft Priority

Large hub airports have gradually become the “bottleneck” of the air transport network. To alleviate the “bottleneck” effect, optimizing the taxi scheduling is one of the solutions. This paper establishes a scheduling optimization model by introducing priority of aircraft under collaborative decision-making mechanism, and a genetic algorithm is designed to verify the scheduling model by simulating. Optimization results show that the reliability of the model and the adjusted genetic algorithm have a high efficiency. The taxiing time decreases by 2.26% when compared with an empirical method and the flights with higher priorities are assigned better taxi routes. It has great significance in reducing flight delays and cost of operation

Location Estimation from an Indoor Selfie

With the development of social networks and hardware devices, many young people have post a lot of high definition v-logs containing selfie images and videos to commemorate and share their daily lives. We found that the reflected image of corneal position in the high definition selfie image has been able to reflect the position and posture of the selfie taker. The classic localization works estimating the position and posture from a selfie are difficult because they lack the knowledge of the environment. The corneal reflection images inherently carry information about the surrounding environment, which can reveal the location, posture and even height of the selfie taker. We analyze the corneal reflection imaging process in the selfie scenario and design a validation experiment based on this process to estimate the pose of the selfie in several scenarios to further evaluate the leakage of the pose information of the selfie taker