# Causal conditional hidden Markov model for multimodal traffic prediction

arXiv: https://arxiv.org/abs/2301.08249 · HTML: https://mlsanity.com/paper/2301.08249/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [Variational Autoencoders and Nonlinear ICA: A Unifying Framework](https://mlsanity.com/paper/1907.04809/) (2019)
- [DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://mlsanity.com/paper/1904.10098/) (2019)
- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution](https://mlsanity.com/paper/2104.14917/) (2021)
- [Learning Causal Semantic Representation for Out-of-Distribution Prediction](https://mlsanity.com/paper/2011.01681/) (2020)
- [Coupled Layer-wise Graph Convolution for Transportation Demand Prediction](https://mlsanity.com/paper/2012.08080/) (2020)
- [Causal Hidden Markov Model for Time Series Disease Forecasting](https://mlsanity.com/paper/2103.16391/) (2021)
- [Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach](https://mlsanity.com/paper/2112.08078/) (2021)
- [Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction](https://mlsanity.com/paper/2101.06954/) (2021)

## Built on by (most cited first, 14 of 14)

- [Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach](https://mlsanity.com/paper/2505.17637/) (2025)
- [Dynamic Causal Disentanglement Model for Dialogue Emotion Detection](https://mlsanity.com/paper/2309.06928/) (2023)
- [Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Prediction](https://mlsanity.com/paper/2408.13293/) (2024)
- [STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models](https://mlsanity.com/paper/2501.03583/) (2025)
- [Nested Spatio-Temporal Time Series Forecasting](https://mlsanity.com/paper/2605.16447/) (2026)
- [Fusion Matrix Prompt Enhanced Self-Attention Spatial-Temporal Interactive Traffic Forecasting Framework](https://mlsanity.com/paper/2410.09356/) (2024)
- [HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting](https://mlsanity.com/paper/2412.00316/) (2024)
- [MSTIM: A MindSpore-Based Model for Traffic Flow Prediction](https://mlsanity.com/paper/2504.13576/) (2025)
- [A Knowledge-Guided Cross-Modal Feature Fusion Model for Local Traffic Demand Prediction](https://mlsanity.com/paper/2509.06976/) (2025)
- [ReBeCA: Unveiling Interpretable Behavior Hierarchy behind the Iterative Self-Reflection of Language Models with Causal Analysis](https://mlsanity.com/paper/2602.06373/) (2026)
- [From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction](https://mlsanity.com/paper/2606.09392/) (2026)
- [Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Prediction](https://mlsanity.com/paper/2608.14177/) (2026)
- [CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effects](https://mlsanity.com/paper/2608.27942/) (2026)
- [When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning](https://mlsanity.com/paper/2609.15305/) (2026)

## Abstract (arXiv)

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.