Airport delay performance evaluation based on fuzzy linear regression model
Article Text (Baidu Translation)
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摘要: 为了有效评估机场延误性能, 考虑到天气和交通变量取值通常会受到一些不确定性因素的影响, 建立了机场到达延误与天气和交通需求之间的模糊线性回归模型。根据模糊线性回归模型得到了估计延误, 通过比较估计延误与实际延误, 可以得到评估延误的连续型性能基准, 并根据性能基准对延误性能进行评估。研究结果表明: 机场到达延误与天气和交通需求之间有较强的线性关系, 可以用线性模型进行拟合。得到的延误性能基准同时考虑了机场天气和交通需求对延误的影响, 将机场到达延误分为低、中、高三种水平, 从而可对不同日的延误水平进行比较评估。Abstract: In order to effectively evaluate the delay performance of airport, taking into account that the weather and traffic variable values were usually subject to some uncertainty factors, the features of traffic demand and weather were used to develop a fuzzy linear regression model for airport arrival delay, and the estimated delay was computed.The continuous baseline to measure operational airport delay performance was obtained by comparing estimated delay with actual delay.The delay performance could be evaluated based on the delay baseline.Analysis result indicates that the features of traffic demand and weather have a strong linear relationship with airport arrival delay, which can be fitted with the model.The delay performance baseline takes into account the impact of airport weather and traffic demand on the delay, and divides the delay into low, medium and high levels, and the delays of different days can be compared and evaluated.
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表 1 模糊线性回归模型的验证
Table 1. Validation of fuzzy linear regression model
序号 yi xi1 xi2 xi3 yi* Ω/% 1 32.60 1 162 11 8.00 30.61 6 2 30.64 1 176 15 8.00 31.17 2 3 36.34 1 178 15 11.10 36.11 1 4 28.39 1 172 13 9.90 33.94 20 5 34.15 1 174 19 12.00 37.83 11 6 57.60 1 167 68 18.10 51.71 10 7 33.01 1 168 23 8.90 33.18 1 8 36.10 1 174 16 7.00 29.64 18 9 29.00 1 170 8 8.00 30.46 5 10 29.48 1 142 12 8.90 31.83 8 11 31.49 1 176 13 8.90 32.42 3 12 25.37 1 168 14 7.00 29.38 16 13 27.99 1 168 6 7.00 28.67 2 14 26.17 1 176 11 8.00 30.81 18 15 37.71 1 167 48 8.90 35.37 6 16 35.05 1 133 7 8.90 31.26 11 17 37.53 1 139 5 12.00 36.08 4 18 43.05 1 156 24 12.00 38.00 12 19 45.74 1 158 40 15.00 44.20 3 20 63.79 1 171 105 20.00 58.05 9 21 44.33 1 117 73 18.10 51.42 16 22 44.44 1 136 71 14.00 45.03 1 23 28.87 1 171 8 8.00 30.48 6 24 28.31 1 161 18 8.90 32.64 15 25 36.21 1 161 25 7.00 30.25 16 26 42.23 1 147 32 13.00 40.16 5 27 35.26 1 137 22 13.00 39.13 11 28 34.27 1 142 18 13.00 38.85 13 29 138.23 1 067 926 19.00 127.44 8 30 85.09 1 114 435 13.00 75.27 12 31 37.13 1 200 90 11.10 43.05 16 
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