Real-time demand forecasting for shared micromobility can greatly enhance its potential benefits and mitigate its adverse effects on urban mobility. The deep learning models provide researchers powerful tools to deal with the real-time dockless scooter-sharing demand prediction problem, but existing studies have not fully incorporated the features that are highly associated with the demand, such as weather conditions, demographic characteristics, and transportation supply. This paper proposes a novel deep learning model named Context-Aware Spatio-Temporal Multi-Graph Convolutional Network (CA-STMGCN) to forecast the real-time spatiotemporal dockless scooter-sharing demand. The proposed model applies a graph convolutional network (GCN) component that uses spatial adjacency graph, functional similarity graph, demographic similarity graph, and transportation supply similarity graph as input to extract spatial dependency and attach it to historical demand data. Then, we use a gated recurrent unit component to process the output of GCN and weather condition data to capture temporal dependency. A fully connected neural network layer is used to generate the final prediction. The proposed model is evaluated using the real-world dockless scooter-sharing demand data in Washington, D.C. The results show that CA-STMGCN significantly outperforms all the selected benchmark models, and the most important model component is the weather information. The proposed model can help the operators develop optimal vehicle rebalancing schemes and guide cities to regulate the dockless scooter-sharing usage.
翻译:深层学习模式为研究人员提供了强有力的工具,以应对实时无码头的小型摩托车共享需求预测问题,但现有研究并未充分纳入与需求高度相关的特征,如天气条件、人口特征和运输供应等。本文提出一个新的深层次学习模式,名为Cecon-Aware Spatio-Temporal-Tember-Graph 多功能网络(CA-STMGCN),以预测实时超时空无码头汽车共享需求。拟议模式采用图式革命网络(GCN)部分,其中使用空间对接图、功能相似图、人口相似图以及运输供应类似图,作为提取空间依赖性的投入,并将其附于历史需求数据。然后,我们使用一个封闭的经常单元,处理GCN和天气状况数据模型的输出,以捕捉时间依赖性。一个完全连接的神经网络层用于产生最后预测。拟议模型使用的是Secondroupal Compal-developal Sloadal Serviceal 计划。拟议模型是使用最现实的Sock-Dhomodel-Cal develop dreal dest dreal developmental developmental developmental developmental development the scolm数据。