A Deep Learning Approach for Short-Term Airport Traffic Flow Prediction

Airport traffic flow prediction is a fundamental research topic in the field of air traffic flow management. Most existing works focus on the single airport traffic flow prediction with temporal dynamics but fail to consider the influence of the topological airport network. In this paper, a novel deep learning-based framework, called airport traffic flow prediction network (ATFPNet), is proposed to capture spatial-temporal dependencies of the historical airport traffic flow (departure and arrival) for the multiple-step situational (network-level) arrival flow prediction. Firstly, considering the nature of the airport distribution and the context of air transportation, a special semantic graph built on the flight schedule is applied to represent the airport network, which is the key to encoding the situational airport traffic flow into a single representation. Then, the graph convolution operator and the gated recurrent unit are combined to extract high-level transition patterns of airport traffic flow in the spatial and temporal dimensions. Finally, a real-world airport traffic flow dataset is applied to validate the effectiveness of the proposed model, and the experimental results demonstrate that the ATFPNet outperforms other baselines on different prediction horizons. Specifically, the proposed method achieves up to 17% MAE improvement compared to baselines. Based on the proposed approach, efficient traffic planning is expected to be achieved for airport management.

Central Authority–Controlled Air Traffic Flow Management: An Optimization Approach

Despite various planning efforts, airspace capacity can sometimes be exceeded, typically because of disruptive events. Air traffic flow management (ATFM) is the process of managing flights in this situation. In this paper, we present an ATFM model that accounts for different rerouting options (path rerouting and diversion) and preexisting en route flights. The model proposes having a central authority to control all decisions, which is then compared with current practice. We also consider interflight and interairline fairness measures in the network. We use an exact approach to solve small- to medium-sized instances, and we propose a modified fix-and-relax heuristic to solve large-sized instances. Allowing a central authority to control all decisions increases network efficiency compared with the case where the ATFM authority and airlines control decisions independently. Our experiments show that including different rerouting options in ATFM can help reduce delays by up to 8% and cancellations by up to 23%. Moreover, ground delay cost has much more impact on network decisions than air delay cost, and network decisions are insensitive to changes in diversion cost. Furthermore, the analysis of the tradeoff between total network cost and overtaking cost shows that adding costs for overtaking can significantly improve fairness at only a small increase in total system cost. A balanced total cost per flight among airlines can be achieved at a small increase in the network cost (0.2%–3.0%) when imposing airline fairness. In conclusion, the comprehensiveness of the model makes it useful for analyzing a wide range of alternatives for efficient ATFM.

Lika Liku Kesuksesan Air Traffic Flow Management di Bandara Internasional I Gusti Ngurah Rai

This research aims to determine and examine mountainously about the satisfaction of airlines and Perum lppnpi management through of air traffic flow management in I Gusti Ngurah Rai International Airport. This research used qualitative method with research subject namely airline, ATFM Operator and Perum lppnpi management. Data collection technique was carried out by in-depth interview. Researchers implement the adoption Zeithaml and Bitner's Servqual Technique. The result of data analysis in this study indicate that the quality of service through ATFM are well guarded, but significantly factor of that quality is reliability, why? Because the consumer has been satisfied with a good service, fast and punctuality.