Abstract: Time Series data are broadly studied in various domains of transportation
systems. Traffic data area challenging example of spatio-temporal data, as it
is multi-variate time series with high correlations in spatial and temporal
neighborhoods. Spatio-temporal clustering of traffic flow data find similar
patterns in both spatial and temporal domain, where it provides better
capability for analyzing a transportation network, and improving related
machine learning models, such as traffic flow prediction and anomaly detection.
In this paper, we propose a spatio-temporal clustering model, where it clusters
time series data based on spatial and temporal contexts. We propose a variation
of a Deep Embedded Clustering(DEC) model for finding spatio-temporal clusters.
The proposed model Spatial-DEC (S-DEC) use prior geographical information in
building latent feature representations. We also define evaluation metrics for
spatio-temporal clusters. Not only do the obtained clusters have better
temporal similarity when evaluated using DTW distance, but also the clusters
better represents spatial connectivity and dis-connectivity. We use traffic
flow data obtained by PeMS in our analysis. The results show that the proposed
Spatial-DEC can find more desired spatio-temporal clusters.