ArXiv Preprint
Multimodal traffic flow can reflect the health of the transportation system,
and its prediction is crucial to urban traffic management. Recent works
overemphasize spatio-temporal correlations of traffic flow, ignoring the
physical concepts that lead to the generation of observations and their causal
relationship. Spatio-temporal correlations are considered unstable under the
influence of different conditions, and spurious correlations may exist in
observations. In this paper, we analyze the physical concepts affecting the
generation of multimode traffic flow from the perspective of the observation
generation principle and propose a Causal Conditional Hidden Markov Model
(CCHMM) to predict multimodal traffic flow. In the latent variables inference
stage, a posterior network disentangles the causal representations of the
concepts of interest from conditional information and observations, and a
causal propagation module mines their causal relationship. In the data
generation stage, a prior network samples the causal latent variables from the
prior distribution and feeds them into the generator to generate multimodal
traffic flow. We use a mutually supervised training method for the prior and
posterior to enhance the identifiability of the model. Experiments on
real-world datasets show that CCHMM can effectively disentangle causal
representations of concepts of interest and identify causality, and accurately
predict multimodal traffic flow.
Yu Zhao, Pan Deng, Junting Liu, Xiaofeng Jia, Mulan Wang
2023-01-19
