考虑行李的多格子元胞自动机登机模型

任新惠; 焦阳; 赵嶷飞

考虑行李的多格子元胞自动机登机模型

1.
中国民航大学经济与管理学院, 天津 300300
2.
天津中科智能技术研究院有限公司, 天津 300309
3.
中国民航大学空中交通管理学院, 天津 300300

基金项目:

国家自然科学基金项目 U1433111

国家自然科学基金项目 U1333108

详细信息

作者简介:
任新惠(1971-), 女, 陕西西安人, 中国民航大学副教授, 从事航空运输企业运营管理研究

中图分类号: V351.17
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- 被引次数: 0
出版历程
- 收稿日期: 2017-05-23
- 刊出日期: 2017-08-25

Multi-grid cellular automata boarding model considering carried baggages

1.
Economics and Management College, Civil Aviation University of China, Tianjin 300300, China
2.
TianJin Intelligent Tech.Institute of Casia Co., Ltd., Tianjin 300309, China
3.
College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

More Information

Author Bio:
REN Xin-hui(1971-), female, associate professor, +86-22-24092464, xinhui9596@sina.com

摘要

摘要: 为了精确描述旅客登机过程, 分析了经典的旅客登机模型, 考虑了旅客携带行李占用过道空间与步行速度, 刻画了2种新的过道干扰: 速度干扰和入座干扰, 建立了多格子元胞自动机登机模型, 根据旅客携带行李数量, 提出了多行李优先登机策略。分析结果表明: 当客座率为100%时, 新模型登机时间为1 455s, 干扰次数为6 720, 经典模型登机时间为1 244s, 干扰次数为5 412, 相比于经典登机模型, 新模型模拟了机舱内旅客运动的复杂情况, 元胞尺寸增大, 旅客间相互作用增强, 因此, 登机时间较长, 过道干扰较大, 比较符合实际旅客登机行为; 当客座率为100%时, 采用多行李优先登机策略的登机时间为1 303s, 相比随机登机策略减少了150s, 节省登机时间10.3%, 过道干扰次数为5 686, 相比随机策略减少了808次, 因此, 采用多行李优先登机策略能有效地减小过道干扰, 提高登机效率。
- 航空运输 /
- 登机策略 /
- 登机模型 /
- 多格子元胞自动机 /
- 过道干扰 /
- 登机效率
Abstract: In order to accurately describe passenger boarding process, the classic boarding model was analyzed. The luggage-occupied aisle space and movement speed of passenger were considered, two new aisle interferences were proposed, namely speed interference and entering seat interference, and a multi-grid cellular automata boarding model was built. A multi-baggage priority boarding strategy was proposed according to the numbers of passengers' baggages. Analysis result shows that when the seat occupancy is 100%, the boarding time and interference number with the new model are 1 455 sand 6 720 times, respectively, while the corresponding values with the classic model are 1 244 sand 5 412 times. Compared with the classic model, in the new model, because the complexity of passenger movement in the cabin is simulated, the cell size and the interaction between passengers increase, so the boarding time is longer, and the aisle interference is larger, which more accords with the actual boarding behavior of passenger. When the seat occupancy is 100%, the multi-baggage priority boarding strategy is compared with the random boarding strategy, the boarding time is 1 303 sand decreases by 150 s, namely 10.3%, and the aisle interference number is 5 686 times and reduces 808 times, so the newboarding strategy can effectively reduce the aisle interference and improve the boarding efficiency.
- air transportation /
- boarding strategy /
- boarding model /
- multi-grid cellular automata model /
- aisle interference /
- boarding efficiency

HTML全文

图 1 机舱座位模型

Figure 1. Cabin seat model

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图 2 T_l随n_b变化趋势

Figure 2. Changing trend of T_l with n_b

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图 3 机舱过道

Figure 3. Aisles in cabin

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图 4 旅客元胞运动模型

Figure 4. Motion model of passenger cell

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图 5 经典模型的旅客登机仿真

Figure 5. Boarding simulation of passengers in classic model

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图 6 新模型的旅客登机仿真

Figure 6. Boarding simulation of passengers in new model

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图 7 新干扰的变化曲线

Figure 7. Changing curves of new interference

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图 8 不同最大速度的登机时间

Figure 8. Boarding times at different maximum speeds

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图 9 不同元胞尺寸下的登机时间

Figure 9. Boarding times under different cellular sizes

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图 10 不同登机模型下的登机时间对比

Figure 10. Comparison of boarding times for different boarding models

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图 11 不同登机模型下的干扰次数对比

Figure 11. Comparison of interference numbers for different boarding models

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图 12 不同登机策略下新增干扰次数对比

Figure 12. Interference number comparison under different boarding strategies

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图 13 不同登机下策略登机时间对比

Figure 13. Boarding time comparison under different boarding strategies

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表 1 行李模型参数

Table 1. Luggage model parameters

下载: 导出CSV

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