My focus on more efficient usage of the runway revolves around big data and analytics. Big data analytics gather vast amounts of data around the airport and analyze it to produce patterns and trends, identify any anomalies, create alerts, and communicate insights. At airports, aeronautical data and information would be captured, managed, and released to increase the general efficiency of operation and, specifically, runway usage.
Firstly, airspace usage efficiency will significantly improve due to timely and more accurate information. Data analytics would support sharing of traffic data in real-time to support departure and landing decisions. Therefore, time wastage will be reduced, and runway throughput will increase. Such information will increase available airspace to allow airports to improve the resilience of their operations. Runway occupancy time (ROT) would also be improved by analyzing real-time surveillance data. Hence, data analytics would support information sharing to create more airspace, reduce congestion, and free the airport.
The airport’s situational awareness (SA) determines the safety achieved. This awareness is only attainable with the dissemination of real-time data. SA is generally defined as perceiving environmental elements within space and time that allow their comprehension and projection of their status shortly (NextGen, 2016). There are four specific components of SA: information extraction from the surroundings, integration of the data with internal knowledge, usage of the results to enhance perceptual exploration, and accurate anticipation of the future. Therefore, SA is founded on collecting accurate environmental data and adequately processing it. The air traffic controllers (ATCO) must collaborate closely with pilots to maintain security. For example, ATCO must constantly communicate with the pilot, other controllers, radar, and all safety nets. A safety breach may occur where the ATCO clears an aircraft flying in IMC and cause it to enter into a cumulonimbus cloud to encounter severe turbulence and icing. Since an efficient runway has high safety standards, data analytics will improve SA to maintain safety.
Reducing ROT depends on accurate and timely data analytics and is a significant determinant of runway throughput. Runway efficiency is essential to airports experiencing capacity constraints, and lower ROT would allow them to accommodate more demand. Such low ROT is attainable if the airport has harnessed large quantities of information regarding its landing operations. This data is analyzed to reveal the most critical factors for ROT and consequently guide the development of reliable methods for ROT reduction. One effective method would be to optimize the runway’s exit systems based on weather data and detailed information from flight tracks. Therefore, data analytics helps reduce ROT and improve runway usage efficiency.
Finally, automation tools that utilize big data are vital in improving available routing options to prevent delays during bad weather. When weather forecasts and conditions change, automated tools assist traffic managers in modifying routing data to increase flexibility and reduce congestion (NextGen, 2016). However, this method would require users’ cooperation in selecting rerouted options and accepting some degree of flexibility in their preferences. Nevertheless, it is an effective method of achieving usage efficiency when weather conditions become unfavorable for flying specific routes.
In conclusion, there are various strategies for improving runway usage efficiency, but big data analytics is the foundation of most approaches. It effectively reduces ROT, offers routing flexibility, increases SA, and improves throughput. Lower ROT will support higher capacity, while flexible routing will prevent delays and congestion during lousy weather. SA is critical for safety when flying and landing, while throughput is a measure of runway efficiency.
Reference
NextGen. (2016). The future of the NAS. Federal Aviation Administration.