Flight schedule optimization considering passengers’ transit duration and transit service selection preference
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摘要:
针对当前时刻优化未充分考虑旅客选择偏好导致航班中转吸引力和机场中转衔接效率较低问题，对旅客中转时长和中转服务选择偏好影响下的航班时刻优化问题进行研究。从中转旅客的实际选择偏好出发，采用选择行为实验收集数据，构建条件Logit模型分析影响旅客中转航班选择行为的航班特性。基于选择偏好分析结果，定义中转航班旅客吸引力参数，以中转航班吸引力最大、可衔接航班配对数最大和总航班时刻调整量最小为目标，建立航班时刻优化模型。通过对比粒子群算法、第2代非支配排序遗传算法（NSGA-Ⅱ）和NSGA-Ⅲ的求解效果，提出考虑中转旅客选择偏好的航班时刻优化方案。结果表明：票价、中转时长和中转便利化服务是影响旅客选择的主要因素；所提方案优化后的航班时刻表中转航班旅客吸引力提升391.22%，可中转衔接航班配对数增加了31.28%，机场中转能力得到有效提升；同时，时刻调整航班占比26.52%，所有调整航班的平均调整量为12.35 min，符合航空公司接受范围。所提方案为航班时刻优化提供了新的视角和方法，有助于提升中国枢纽中转能力，便利旅客中转出行。
Abstract:Addressing the issue of low attractiveness and inefficient airport transit connections due to current schedule optimizations that do not fully consider passengers’ selection preferences, an investigation into flight schedule optimization influenced by passengers’ transit duration and transit service selection preferences was conducted. Starting from the actual selection preferences of transfer passengers, a selection behavior experiment was conducted to collect data, and a conditional Logit model was constructed to analyze the flight characteristics that influence passengers’ choices for transit flights. Based on the findings of the selection preference analysis, a passenger attractiveness parameter for transit flights was defined. Subsequently, a flight schedule optimization model was developed with the objectives of maximizing the attractiveness of transit flights, maximizing the number of connectable flight pairs, and minimizing the total flight schedule adjustment. By comparing the solution effectiveness of particle swarm, non-dominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ）, and NSGA-Ⅲ, a flight schedule optimization scheme that considers the selection preferences of transfer passengers was proposed. The results indicate that fares, transit duration, and transit facilitation services are the primary factors affecting passengers’ selection behavior. The results demonstrate that the proposed optimized flight schedule is significantly more attractive to transfer passengers, with the attractiveness of transit flights increasing by 391.22%, the number of connectable flight pairs increasing by 31.28%, and the airport’s transit capacity being effectively enhanced. Additionally, 26.52% of flights were adjusted, with an average adjustment of 12.35 minutes, which falls within the airlines’ acceptable range. This study offers new perspectives and methods for flight schedule optimization, contributing to the enhancement of Chinese hub airports’ transit capacity and facilitating passengers’ travel experiences.
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图 8 优化前后航班时刻结构（5 min）
Figure 8. Flight schedule structure before and after optimization (5 min)
图 10 优化后进港航班的中转机会变化分布
Figure 10. Distribution of changes in transit opportunities for arrival flights after optimization
表 1 条件Logit模型拟合结果
Table 1. Conditional Logit model fitting results
变量 回归系数 Z值 p值 OR值 CI(OR值95%) $ {X_1} $ −0.307*** 0.047 0 0.735 0.671～0.806 $ {X_2} $ −0.377*** 0.053 0 0.686 0.618～0.762 $ {X_3} $ 0.236*** 0.050 0 1.266 1.149～1.396 $ {X_0} $ −0.271*** 0.068 0 0.763 0.668～0.871 注：***表示系数在1%的水平上显著，即p<0.01。 
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表 2 兰州中川机场原始进港航班计划（部分）
Table 2. Lanzhou Zhongchuan airport original arrival flight plan (partial)
航班号 出发机场 计划起飞时刻 到达机场 计划降落时刻 TV6061 XIY 06:25 LHW 08:00 TV6046 INC 07:10 LHW 08:15 G54653 CKG 06:55 LHW 08:40 GJ8295 CGO 06:30 LHW 08:45 DR5323 DNH 07:00 LHW 08:55 GS7877 TSN 06:35 LHW 08:55 3U8361 CTU 07:40 LHW 09:05 MU2471 TFU 07:40 LHW 09:05 TV9913 LXA 07:00 LHW 09:10 CA2581 TFU 07:35 LHW 09:15
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表 3 兰州中川机场原始离港航班计划（部分）
Table 3. Lanzhou Zhongchuan airport original departure flight plan (partial)
航班号 出发机场 计划起飞时刻 到达机场 计划降落时刻 9C6737 LHW 07:00 AKA 08:25 CZ5370 LHW 06:00 SHE 08:50 MU2195 LHW 07:00 CGO 08:50 MU2249 LHW 07:15 KMG 09:20 9C6533 LHW 08:30 IQN 09:25 MU2373 LHW 07:55 TYN 09:30 9C6305 LHW 07:25 TSN 09:40 9C7087 LHW 07:00 HGH 09:50 MU6810 LHW 07:10 PVG 09:50 9C6187 LHW 07:20 NKG 09:50
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表 4 不同算法收敛后的IGD
Table 4. IGD values after convergence of different algorithms
算法 IGD PSO 0.265 5×10−7 NSGA-Ⅱ 0.204 0×10−7 NSGA-Ⅲ 0.149 9×10−7
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表 5 优化后进港航班时刻调整结果（部分）
Table 5. Results of arrive flight schedule adjustments (partial)
航班号 起飞机场 降落机场 优化前时刻 优化后时刻 调整量/min TV6046 INC LHW 8:15 8:05 +10 UQ3562 HTN LHW 12:10 12:15 +5 CZ6491 SHE LHW 12:15 12:25 +10 CA1207 PEK LHW 12:25 12:50 +25 9C6134 HFE LHW 13:05 13:10 +5 ZH9237 SZX LHW 13:20 13:15 −5 9C6188 NKG LHW 13:40 13:15 −25 MU2412 PKX LHW 13:50 13:20 −30 MU6499 TNA LHW 13:55 13:30 −25
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表 6 优化后离港航班时刻调整结果（部分）
Table 6. Results of departure flight schedule adjustments (partial)
航班号 起飞机场 降落机场 优化前时刻 优化后时刻 调整量/min HU7537 LHW DSN 8:40 8:50 +10 HU7553 LHW SJW 8:40 8:50 +10 MU9873 LHW JGN 9:30 9:55 +25 3U3675 LHW CZX 9:40 9:55 +15 3U8362 LHW CTU 9:55 10:20 +25 TV9914 LHW LXA 9:55 10:25 +30 CA2582 LHW TFU 10:20 10:35 +15 NS3310 LHW SJW 10:25 10:40 +15 CA8598 LHW WNZ 10:50 10:55 +5
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表 7 优化前后各目标函数值
Table 7. Value of each objective function before and after optimization
优化 Z1 Z2/对 Z3/min 优化前 27.57 1071 优化后 135.43 1406 1075
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表 8 优化后中转机会增量较大的进港航班
Table 8. Arrival flights with large incremental transit opportunities after optimization
进港航班 始发机场 优化前
中转机会数/个优化后
中转机会数/个MU2415 敦煌机场 17 32 MU2417 嘉峪关机场 15 30 HO1102 金昌机场 23 33 TV6062 林芝机场 15 25 3U3590 甘孜机场 13 24 MU9677 嘉峪关机场 4 14 HU7420 新疆库尔勒机场 4 14 9C6186 敦煌机场 1 14
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