  Traffic speed prediction is significant for intelligent navigation and
congestion alleviation. However, making accurate predictions is challenging due
to three factors: 1) traffic diffusion, i.e., the spatial and temporal
causality existing between the traffic conditions of multiple neighboring
roads, 2) the poor interpretability of traffic data with complicated
spatio-temporal correlations, and 3) the latent pattern of traffic speed
fluctuations over time, such as morning and evening rush. Jointly considering
these factors, in this paper, we present a novel architecture for traffic speed
prediction, called Interpretable Causal Spatio-Temporal Diffusion Network
(ICST-DNET). Specifically, ICST-DENT consists of three parts, namely the
Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and
Speed Fluctuation Pattern Recognition (SFPR) modules. First, to model the
traffic diffusion within road networks, an STCL module is proposed to capture
both the temporal causality on each individual road and the spatial causality
in each road pair. The CGG module is then developed based on STCL to enhance
the interpretability of the traffic diffusion procedure from the temporal and
spatial perspectives. Specifically, a time causality matrix is generated to
explain the temporal causality between each road's historical and future
traffic conditions. For spatial causality, we utilize causal graphs to
visualize the diffusion process in road pairs. Finally, to adapt to traffic
speed fluctuations in different scenarios, we design a personalized SFPR module
to select the historical timesteps with strong influences for learning the
pattern of traffic speed fluctuations. Extensive experimental results prove
that ICST-DNET can outperform all existing baselines, as evidenced by the
higher prediction accuracy, ability to explain causality, and adaptability to
different scenarios.
