  Accurate acquisition of crowd flow at Points of Interest (POIs) is pivotal
for effective traffic management, public service, and urban planning. Despite
this importance, due to the limitations of urban sensing techniques, the data
quality from most sources is inadequate for monitoring crowd flow at each POI.
This renders the inference of accurate crowd flow from low-quality data a
critical and challenging task. The complexity is heightened by three key
factors: 1) The scarcity and rarity of labeled data, 2) The intricate
spatio-temporal dependencies among POIs, and 3) The myriad correlations between
precise crowd flow and GPS reports.
  To address these challenges, we recast the crowd flow inference problem as a
self-supervised attributed graph representation learning task and introduce a
novel Contrastive Self-learning framework for Spatio-Temporal data (CSST). Our
approach initiates with the construction of a spatial adjacency graph founded
on the POIs and their respective distances. We then employ a contrastive
learning technique to exploit large volumes of unlabeled spatio-temporal data.
We adopt a swapped prediction approach to anticipate the representation of the
target subgraph from similar instances. Following the pre-training phase, the
model is fine-tuned with accurate crowd flow data. Our experiments, conducted
on two real-world datasets, demonstrate that the CSST pre-trained on extensive
noisy data consistently outperforms models trained from scratch.
