Spatio-temporal neural structural causal models for bike flow prediction

Understand

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.