  In the landscape of spatio-temporal data analytics, effective trajectory
representation learning is paramount. To bridge the gap of learning accurate
representations with efficient and flexible mechanisms, we introduce Efflex, a
comprehensive pipeline for transformative graph modeling and representation
learning of the large-volume spatio-temporal trajectories. Efflex pioneers the
incorporation of a multi-scale k-nearest neighbors (KNN) algorithm with feature
fusion for graph construction, marking a leap in dimensionality reduction
techniques by preserving essential data features. Moreover, the groundbreaking
graph construction mechanism and the high-performance lightweight GCN increase
embedding extraction speed by up to 36 times faster. We further offer Efflex in
two versions, Efflex-L for scenarios demanding high accuracy, and Efflex-B for
environments requiring swift data processing. Comprehensive experimentation
with the Porto and Geolife datasets validates our approach, positioning Efflex
as the state-of-the-art in the domain. Such enhancements in speed and accuracy
highlight the versatility of Efflex, underscoring its wide-ranging potential
for deployment in time-sensitive and computationally constrained applications.
