基于时空融合图的共享单车需求预测系统

Spatial-Temporal Fusion Graph Based Bike-Sharing Demand Prediction Systems

摘要:

作为一种新的城市公共交通方式，共享单车的出现给人们的日常出行带来了极大的便利性；但由于其使用的过程中存在动态不均衡的性质，用户常会面临无车可借和无桩可还的现象，因此需要一个精确的需求预测模型，来解决共享单车的调度问题；为此提出一种新的基于时空融合图的注意力网络模型，该模型将不同时间片的相同车站节点进行连接，从而形成一张大的时空融合图；得益于时空融合图下的自注意力机制，模型在时间相关性和空间相关性之外，还可以捕获局部时空相关性和全局时空相关性；在Divvy数据集上的实验表明，该模型的预测精度优于以往的时空数据预测模型，预测结果与真实结果曲线相吻合，可为实际中共享单车需求预测系统提供有效参考。

Abstract:

As a new mode of urban public transportation, the emergence of shared bicycles has brought great convenience to people's daily travel; however, due to the dynamic and uneven nature of their usage, users often face the phenomenon of no bikes available for borrowing and no stakes available for returning, so an accurate demand prediction model is needed to solve the scheduling problem of shared bicycles; to this end, a new attention network model based on spatial-temporal fusion graph is proposed, which connects the same station nodes of different time slices to form a large spatial-temporal fusion graph; thanks to the self-attention mechanism under the spatial-temporal fusion graph, the model can capture local spatial-temporal correlation and global spatial-temporal correlation; experiments on the Divvy dataset show that the prediction accuracy of the model is better than previous spatial-temporal data prediction models, and the prediction results match the real result curves, which can provide an effective reference for bike-sharing demand prediction systems in practice.