Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction

Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised…

• NN
  National Natural Science Foundation of China

  Awards: 62172034, 82161148011, 72171013, 72222022
• BN
  Beijing Nova Program

  Award: Z201100006820053
• FR
  Fundamental Research Funds for the Central Universities