Forecasting Traffic Volume of Urban Logistics Drones in Low-altitude Airspace
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摘要: 为探究城市物流的无人机配送需求及低空空域交通量，提出了1种考虑公众意愿程度的无人机配送包裹量预测方法。基于二元Logistic回归模型构建了无人机配送的公众使用意愿模型，综合考虑公众使用意愿和包裹标签（即包裹是否去往国内或国外、是否去往城市或农村、载具是否为无人机、是否低于特定重量等）估算了城市无人机配送的需求量。以广州、北京等5个城市为例进行了2025—2050年城市无人机配送需求量和低空空间交通量的预测实验。结果表明：经模型显著性验证，公众使用意愿的整体预测精度为81.7%。2025—2050年无人机配送的需求量均呈现上升态势；城市居民人口、经济发展、公众使用意愿等因素都将影响城市无人机配送的发展；考虑公众使用意愿因素能够提高无人机配送需求量预测的可靠性，预测结果可为城市低空交通空域规划提供指导。
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关键词:
- 城市交通 /
- 无人机物流 /
- 低空空域交通量 /
- Logistic模型
Abstract: In order to estimate the demand of urban parcel delivery using unmanned air vehicles (UAVs) and the corresponding traffic volume in low-altitude airspace, a method is proposed for predicting traffic volume of UAVs delivering parcels based on the consideration of the public's intention and a binary Logistic model. The demand of instant delivery using UAVs will be estimated by considering the public's intention and parcel labels(i.e. whether the package goes to domestic or foreign, whether it goes to urban or rural areas, whether the carrier is a drone, whether it is below a specific weight, etc.). A case study of five cities, including Guangzhou and Beijing, is completed to forecast the parcel volume that can be delivered by UAVs and the corresponding traffic volume of UAVs in the low-altitude airspace from 2025 to 2050. The results show that the accuracy of the prediction accuracy of the public's intention on UAV distribution is 81.7% and the demand of urban parcel delivery using UAVs will be on the rise from 2025 to 2050. The population of urban residents, economic development, the public's intention and other factors will affect the development of UAV-based parcel distribution. Study results show that the reliability of forecasting the demand of UAV delivery can be improved by taking account into the public's intention on UAV distribution and such predictions can be used to support the low-altitude airspace planning, in order to accommodate the rise of parcel delivery using UAVs.-
Key words:
- urban traffic /
- drone logistics /
- low-altitude airspace traffic /
- logistic model
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图 2 全国5座城市低、中、高增长率下无人机交付的包裹量预测图
Figure 2. Forecast map of package volume delivered by drones under low, medium, and high growth rates in five cities across the country
图 3 全国5座城市空域无人机每小时交通流量预测图
Figure 3. Hourly traffic flow forecast of drones in the airspace of five cities across the country
表 1 个人属性变量解释及赋值表
Table 1. Personal attribute variable interpretation and assignment table
变量类别 变量名称 变量代码 变量解释及赋值 取值范围 个人社会属性 年龄/岁 A 18及以下=1；9~34=2；5~49=3;0及以上=4 1~4 性别 G 男=1;女=2 1~2 职业 P 学生=1;企事业单位=2;自由职业者=3;其他=4 1~4 学历 E 本科以下=1;本科=2;硕士=3;博士=4 1~4 月收人/元 I 5 000以下=1; 000~10 000=2 10 000以上=3 1~3 个人认知属性 职业相关度 J 从事与无人机配送相关职业，采用五级李克量表 1~5 安全关注度 S 骑手配送过程中引发的安全问题，采用五级李克量表 1~5 新事物接受度 F 愿意接受或尝试新事物的程度，采用五级李克量表 1~5 
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表 2 感知因素变量解释及赋值表
Table 2. Perception factor variable interpretation and assignment table
变量类别 变量代码 测量变量名称 感知有用性 X1 无人机配送相较于现行配送更节约时间 X2 无人机配送相较于现行配送更节省费用 X3 无人机配送相较于现行配送更加环保 X4 无人机配送普及后可提供新的工作岗位 感知易用性 X5 您觉得您是都能很快学会无人机装取货操作 X6 您是否在意无人机无法实现上门取、送货 感知风险性 X7 您是否担心无人机在配送中发生故障进而延误时间或伤害行人、其他财务损失(如坠落) X8 您是否担心无人机在配送过程中发生错误(如送错地址、丢失包裹） X9 您是否担心无人机大量从事配送会侵犯您的隐私 感知可行性 X10 我认为无人机城市配送是可行的 X11 我认为无人机配送是未来的主流配送方式
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表 3 样本人数基本特征描述性统计表
Table 3. Descriptive statistics table of basic characteristics of sample size
个人属性变量 总样本数 愿意使用人数 百分比/% 不愿意使用人数 百分比/% 年龄/岁 18及以下 16 10 62.50 6 37.50 19~34 413 270 65.38 143 34.62 35~49 98 58 59.18 40 40.82 50及以上 20 12 60.00 8 40.00 性别 男 257 162 63.04 95 36.96 女 290 188 64.83 102 35.17 职业 学生 186 123 66.13 63 33.87 企事业单位 266 165 62.03 101 37.97 自由职业者 49 33 67.35 16 32.65 其他 46 29 63.04 17 36.96 学历 本科以下 100 68 68.00 32 32.00 本科 278 170 61.15 108 38.85 硕士 146 97 66.44 49 33.56 博士 23 15 65.22 8 34.78 月收人/元 5 000以下 267 176 65.92 91 34.08 5 000-10 000 174 115 66.09 59 33.91 10 000以上 106 59 55.66 47 44.34
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表 4 信度检验表
Table 4. Reliability checklist
Cronbach α 基于标准化项目的Cronbach α 项目个数 0.722 0.701 20
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表 5 Hosmer-Lemeshow检验表
Table 5. Hosmer-Lemeshow checklist
步骤 卡方 df 显著性 1 17.525 8 0.025 15 8.920 8 0.349
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表 6 二项Logistic模型回归分析结果
Table 6. Binomial Logistic model regression analysis results
变量名 B S.E. Wald df 显著性 Exp(B) 95% Exp(B)之信赖区间 下限 上限 X5 0.501 0.127 15.548 1 0.000 1.650 1.287 2.117 X1 0.385 0.143 7.261 1 0.007 1.470 1.111 1.946 X7 -0.348 0.139 6.234 1 0.013 0.706 0.537 0.928 X10 0.877 0.147 35.468 1 0.000 2.404 1.801 3.208 X11 0.754 0.152 24.731 1 0.000 2.124 1.579 2.859 常数 -6.985 0.821 72.409 1 0.000 0.001
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表 7 预测结果表
Table 7. Forecast classification table
预测内容 预测正确数 预测错误数 预测准确率/% 不愿意使用无人机配送包裹 132 65 67.0 愿意使用无人机配送包裹 315 35 90.0
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表 8 低、中、高增长率下无人机交付的包裹量预测表
Table 8. Forecast table of package volume delivered by drones under low, medium and high growth rates
单位: 亿件 年份 包裹量 低增长率 中增长率 高增长率 2025 234.04 283.40 341.16 2030 275.95 391.95 550.69 2035 325.38 542.06 888.92 2040 383.65 749.67 1 434.86 2045 452.37 1 036.80 2 316.12 2050 533.39 1 433.89 3 738.62
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表 9 全国城市空域无人机每小时交通量预测表
Table 9. Hourly UAV traffic forecast table for national urban airspace
单位: 架·次 年份 空无人机数量 低增长率 中增长率 高增长率 2025 4 673 239 5 659 026 6 812 326 2030 5 510 238 7 826 432 10 996 276 2035 6 497 149 10 823 952 17 749 900 2040 7 660 819 14 969 523 28 651 422 2045 9 032 909 20 702 847 46 248 367 2050 10 650 747 28 632 033 74 652 893
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