As a representative of public transportation, the fundamental issue of managing bike-sharing systems is bike flow prediction. Recent methods overemphasize the spatio-temporal correlations in the data, ignoring the effects of contextual conditions on the transportation system and the inter-regional timevarying causality. In addition, due to the disturbance of incomplete observations in the data, random contextual conditions lead to spurious correlations between data and features, making the prediction of the model ineffective in special scenarios. To overcome this issue, we propose a Spatio-temporal Neural Structure Causal Model(STNSCM) from the perspective of causality. First, we build a causal graph to describe the traffic prediction, and further analyze the causal relationship between the input data, contextual conditions, spatiotemporal states, and prediction results. Second, we propose to apply the frontdoor criterion to eliminate confounding biases in the feature extraction process. Finally, we propose a counterfactual representation reasoning module to extrapolate the spatio-temporal state under the factual scenario to future counterfactual scenarios to improve the prediction performance. Experiments on real-world datasets demonstrate the superior performance of our model, especially its resistance to fluctuations caused by the external environment. The source code and data will be released.
翻译:作为公共交通的代表,管理自行车共享系统的根本问题是自行车流动预测。最近的方法过分强调数据中的时空相关性,忽视了相关条件对运输系统的影响以及区域间时间变化因果关系。此外,由于数据中观测不全的干扰,随机背景条件导致数据和特征之间的虚假关联,使得在特殊情况下对模型的预测无效。为了克服这一问题,我们从因果关系的角度提出一个Spatio-时空神经结构因果关系模型(STNSCM ) 。首先,我们建立一个因果图表来描述交通预测,并进一步分析输入数据、背景条件、时空状态和预测结果之间的因果关系。第二,我们提议采用前门标准来消除特征提取过程中的偏差。最后,我们提出一个反事实代表性推论模块,在事实假设中将空间时空状态与未来反事实假设相推,以改进预测性绩效。对现实世界数据环境的实验将显示其高水平的性能,特别是由外部数据变化导致的外部数据源。