了解网络结构对不同规模的航空旅行模式的影响
Hoai Nguyen Huynh1, Kuan Luen Ng2, Roy Toh2
1Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
PloS one
|March 8, 2024
概括
复杂的网络分析显示,PageRank最好预测全球航空旅行需求. 亚洲的机场显示出更高的流量,而欧洲和北美的枢纽可以吸引更多的乘客. 这增强了需求建模.
科学领域:
- 复杂系统科学 复杂系统科学
- 交通地理 交通地理
- 网络分析 网络分析
背景情况:
- 全球航空旅行需求是一个复杂的现象,受众因素的影响.
- 预测航空旅行需求的传统模型通常依赖于社会经济变量.
- 了解机场的网络结构对于分析旅行模式至关重要.
研究的目的:
- 调查机场网络措施与其乘客数量之间的关系.
- 为全球航空旅行需求确定最有效的网络分析模型.
- 探索航空旅行交通及其驱动因素的空间分布.
主要方法:
- 复杂的网络分析应用于50个顶级机场的全球飞行数据.
- 评估各种网络指标 (例如,PageRank,基于距离) 和属性 (例如,路线长度,航空公司数量).
- 网络措施与社会经济变量 (人口,GDP) 的整合.
主要成果:
- 网页排名 (PageRank) 成为与总乘客数量最相关的网络指标.
- 强调航空公司数量的网络措施优于基于距离的措施.
- 亚洲城市的机场显示出高于预期的流量,而欧洲和北美机场显示出增长的潜力.
结论:
- 网络分析,特别是使用航空公司数量加权的PageRank,为全球航空旅行需求提供了有价值的见解.
- 网络措施显著增强了对需求建模和预测的传统社会经济方法.
- 这些发现对机场规划和航空业战略有影响.
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