Air passenger forecasting provides important insights for both Governments and Aerospace industries to plan their for their future activities. Google Trends can provide a large database of historical search query frequency which can be used as explanatory variables for air passenger forecasting. This paper explores the use of a Neural Granger Causality model to select the best search query that can forecast arrival air passengers in Singapore Changi Airport. Neural Granger Causality models are an extension of the original Granger Causality model that uses neural networks instead of Linear Vector Auto-Regressive (VAR) models to capture non-linear relations between the targets and the tested explanatory variables. In this paper, 1317 Google Trends search queries are tested for Neural Granger Causality of which 171 queries are deemed as Neural Granger Causal for forecasting Singapore Changi Airport monthly arrival passengers. The model that used all 171 Neural Granger Queries achieved the highest $R^2$ value ($R^2=0.919$) with the lowest Standard Deviation ($SD=0.363$) compared to the other models which was not filtered for Neural Granger Causality. The 171 queries found are search terms that reflects a unidirectional neural granger causal relationship with the number of arrival air passengers at Changi Airport.

航空旅客预测为政府和航空航天行业规划未来活动提供了重要见解。Google Trends 可以提供历史搜索查询频率的大型数据库，可用作航空旅客预测的解释变量。本文探讨了使用神经格兰杰因果关系模型来选择可以预测新加坡樟宜机场到达的航空旅客的最佳搜索查询。神经格兰杰因果关系模型是原始格兰杰因果关系模型的扩展，该模型使用神经网络代替线性向量自回归 (VAR) 模型来捕获目标与测试解释变量之间的非线性关系。在本文中，1317 条 Google 趋势搜索查询针对神经格兰杰因果关系进行了测试，其中 171 条查询被视为神经格兰杰因果关系，用于预测新加坡樟宜机场每月到达的乘客。使用所有 171 个神经格兰杰查询的模型取得了最高的成绩${\mathrm{电阻}}^2$ 价值 （${\mathrm{电阻}}^2=0.919$) 具有最低标准偏差 ($秒D=0.363$) 与未过滤神经格兰杰因果关系的其他模型相比。找到的 171 个查询是搜索词，它们反映了与樟宜机场到达的航空乘客数量之间存在单向神经格兰杰因果关系。