Short/medium-term prediction for the aviation emissions in the en route airspace considering the fluctuation in air traffic demand |
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| Institution: | 1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, PR China;2. Department of Civil and Environmental Engineering, Imperial College London, SW7 2BU, UK;1. Transportation Management College, Dalian Maritime University, Dalian 116026, China;2. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China;3. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China;4. School of Business Administration, Nanjing University of Finance and Economics, Nanjing 210023, China;5. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China;1. Graduate School of Sciences, Anadolu University, TR-26470, Eskişehir, Turkey;2. School of Civil Aviation, Süleyman Demirel University, TR-32700, Isparta, Turkey;3. Department of Mechanical Engineering, Faculty of Engineering, Dokuz Eylul University, Buca, Izmir, TR-35397, Turkey;4. Faculty of Aeronautics and Astronautics, Anadolu University, TR-26470, Eskişehir, Turkey;1. Faculty of Management and Economics, Dalian University of Technology, No. 2 Linggong Road, Dalian City, 116024, China;2. Transportation Management College, Dalian Maritime University, No. 1 Linghai Road, Dalian City, 116026, China;1. International Telematic University Uninettuno, Italy;2. University of Turin, Italy;3. LUISS \"Guido Carli\" University of Rome, and University of Foggia, Italy |
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| Abstract: | This paper proposes a novel short/medium-term prediction method for aviation emissions distribution in en route airspace. An en route traffic demand model characterizing both the dynamics and the fluctuation of the actual traffic demand is developed, based on which the variation and the uncertainty of the short/medium-term traffic growth are predicted. Building on the demand forecast the Boeing Fuel Flow Method 2 is applied to estimate the fuel consumption and the resulting aviation emissions in the en route airspace. Based on the traffic demand prediction and the en route emissions estimation, an aviation emissions prediction model is built, which can be used to forecast the generation of en route emissions with uncertainty limits. The developed method is applied to a real data set from Hefei Area Control Center for the en route emission prediction in the next 5 years, with time granularities of both months and years. To validate the uncertainty limits associated with the emission prediction, this paper also presents the prediction results based on future traffic demand derived from the regression model widely adopted by FAA and Eurocontrol. The analysis of the case study shows that the proposed method can characterize well the dynamics and the fluctuation of the en route emissions, thereby providing satisfactory prediction results with appropriate uncertainty limits. The prediction results show a gradual growth at an average annual rate of 7.74%, and the monthly prediction results reveal distinct fluctuation patterns in the growth. |
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| Keywords: | Aviation emissions prediction Air traffic demand Boeing Fuel Flow Method 2 En route airspace |
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