论文标题
STDI-NET：具有动态间隔映射的空间网络，用于自行车共享需求预测
STDI-Net: Spatial-Temporal Network with Dynamic Interval Mapping for Bike Sharing Demand Prediction
论文作者
论文摘要
作为一种经济和健康的共享运输方式，自行车共享系统（BSS）在许多大城市中迅速发展。准确的预测方法可以帮助BSS提前安排资源以满足用户的需求，并肯定提高了其操作效率。但是，大多数用于类似任务的现有方法只是独立利用空间或时间信息。尽管有一些方法同时考虑两者，但它们仅关注单个位置或位置对之间的需求预测。在本文中，我们提出了一种新颖的深度学习方法，称为时空动态间隔网络（STDI-NET）。该方法通过对关节时空信息进行建模，可以预测多个连接站点的租赁和返回订单的数量。此外，我们嵌入了一个附加的模块，该模块在不同的时间间隔中生成动力学可学习的映射，以包括不同时间间隔对BSS中需求预测有很大影响的因素。在纽约自行车数据集上进行了广泛的实验，结果证明了我们方法比现有方法的优越性。
As an economical and healthy mode of shared transportation, Bike Sharing System (BSS) develops quickly in many big cities. An accurate prediction method can help BSS schedule resources in advance to meet the demands of users, and definitely improve operating efficiencies of it. However, most of the existing methods for similar tasks just utilize spatial or temporal information independently. Though there are some methods consider both, they only focus on demand prediction in a single location or between location pairs. In this paper, we propose a novel deep learning method called Spatial-Temporal Dynamic Interval Network (STDI-Net). The method predicts the number of renting and returning orders of multiple connected stations in the near future by modeling joint spatial-temporal information. Furthermore, we embed an additional module that generates dynamical learnable mappings for different time intervals, to include the factor that different time intervals have a strong influence on demand prediction in BSS. Extensive experiments are conducted on the NYC Bike dataset, the results demonstrate the superiority of our method over existing methods.
