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摘要:
为优化综合交通枢纽服务流程，分析了到港旅客聚集行为动力学特性，研究了到港旅客聚集行为在逆伽马分布、伽马分布与卡方分布时间维度间的转变机制，揭示了聚集行为受航班运行时间与换乘方式效用共同影响，并分别转化为到港旅客时间价值与效用价值。在此基础上，基于逆伽马分布、伽马分布与卡方分布建立了双价值驱动的到港旅客动力学模型，输出为参数可调的到港旅客分布。结果表明：参数可调的仿真输出与真实分布相吻合，为枢纽机场到港旅客分布态势的精准预测提供了方法和依据。
Abstract:In order to optimize the service process of the integrated transportation hub, the dynamic characteristics of the aggregation behavior of arriving passengers were analyzed. The transition mechanism of arriving passenger aggregation behavior between inverse gamma distribution, gamma distribution, and chi-square distribution was studied. It revealed that this aggregation behavior was affected by the flight operation time and transfer mode utility, which was converted into the time value and utility value of arriving passengers, respectively. On this basis, a dynamic model of arriving passengers driven by the dual values was established based on these three distributions, and the output was the distribution of arriving passengers with adjustable parameters. The results show that the simulation output with adjustable parameters is consistent with the real distribution, which provides a method and basis for accurate prediction of the distribution situation of arriving passengers at the airport.
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Key words:
- human dynamics /
- arriving passengers /
- gamma distribution /
- time value function /
- utility value function
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图 1 航班平均到达班次与可供应停机位数量的比值
Figure 1. Ratio of average flight arrivals to the number of available parking spaces
图 2 不同时段内某区间统计到达口旅客分布
Figure 2. Distribution of passengers at arrival gate in a certain interval from different time
图 3 不同时段内某区间统计到达口旅客仿真输出
Figure 3. Simulation output of passengers at arrival gate in a certain interval from different time
表 1 到港航班信息
Table 1. Arrival flight information
航班 到港
时间上轮挡
时间开舱门时间 机位 到港
人数总行
李数1 9:00:00 9:04:00 9:12:20 202 110 52 2 9:07:00 9:10:00 9:13:52 206 173 59 3 9:10:00 9:15:00 9:16:40 202 70 28 $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ 
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表 2 到达口旅客人数
Table 2. Number of passengers at the arrival gate
调查点位 人数 时刻 时段 旅客出口A 旅客出口C 8:10 0～5 min 151 41 5～10 min 183 68 10～15 min 68 114 16:30 0～5 min 121 76 5～10 min 273 132 10～15 min 179 37 18:00 0～5 min 25 116 5～10 min 63 92 10～15 min 100 40
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表 3 到港旅客分布拟合参数及拟合度检验
Table 3. Arriving passenger distribution fitting parameters and fit test
时段 卡方分布 伽马分布 逆伽马分布 $\nu $ R2 $\alpha $ $\beta $ R2 $\alpha $ $\beta $ R2 6:00-9:59 5.544 0.680 1.985 3.644 0.672 1.981 10.102 0.869 10:00-17:59 10.136 0.527 3.564 3.192 0.810 1.746 16.273 0.499 18:00-23:59 7.882 0.786 2.708 3.346 0.676 1.873 12.524 0.637
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表 4 时间价值驱动的各参数
Table 4. Parameter driven by the time value
时间感知数值 行李总数均值 $B$ ${V_{{\text{APTV}}}}$ ${b_{\text{1}}}$ $ L $ ${b_{\text{2}}}$ 0.9163 0.8602 1.0998 0.6344 3.039
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表 5 效用价值驱动的各参数
Table 5. Parameter driven by the utility value
出行方式 费用 时间 舒适度 $ \xi $ $ A $ ${a_{\text{1}}}$ $M$ ${a_{\text{2}}}$ $T$ ${a_{\text{3}}}$ $C$ 私人交通 0.1 1 0.45 0.5 0.45 1 1 3.653 公共交通 0.8 0.5 0.1 1 0.1 0.5 0.5
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表 6 不同分布的仿真拟合与实际拟合的差异程度
Table 6. Degree of difference between simulation fitting and distribution actual fitting of different distributions
评价指标 伽马分布 逆伽马分布 卡方分布 KLD 0.337 0.443 0.257
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