Terroso-Sáenz Fernando, Muñoz Andrés
UCAM, Campus de los Jerónimos, Guadalupe, 30107 Murcia España.
Appl Intell (Dordr). 2022;52(4):4144-4160. doi: 10.1007/s10489-021-02645-3. Epub 2021 Jul 19.
Nowadays, the anticipation of human mobility flow has important applications in many domains ranging from urban planning to epidemiology. Because of the high predictability of human movements, numerous successful solutions to perform such forecasting have been proposed. However, most focus on predicting human displacements on an intra-urban spatial scale. This study proposes a predictor for nation-wide mobility that allows anticipating inter-urban displacements at larger spatial granularity. For this goal, a Graph Neural Network (GNN) was used to consider the latent relationships among large geographical regions. The solution has been evaluated with an open dataset including trips throughout the country of Spain and the current weather conditions. The results indicate a high accuracy in predicting the number of trips for multiple time horizons, and more important, they show that our proposal only needs a single model for processing all the mobility areas in the dataset, whereas other techniques require a different model for each area under study.
如今，对人类流动趋势的预测在从城市规划到流行病学等许多领域都有重要应用。由于人类移动具有较高的可预测性，已经提出了许多成功的此类预测解决方案。然而，大多数研究都集中在城市内部空间尺度上预测人类位移。本研究提出了一种用于全国范围内人口流动的预测器，该预测器能够在更大的空间粒度上预测城市间的位移。为实现这一目标，使用了图神经网络（GNN）来考虑大地理区域之间的潜在关系。该解决方案已通过一个开放数据集进行评估，该数据集包括西班牙全国的出行情况和当前天气状况。结果表明，在预测多个时间范围内的出行数量方面具有很高的准确性，更重要的是，结果表明我们的方案仅需一个单一模型就能处理数据集中的所有流动区域，而其他技术则需要为每个研究区域建立不同的模型。
