# Hybrid hidden Markov LSTM for short-term traffic flow prediction

arXiv: https://arxiv.org/abs/2307.04954 · HTML: https://mlsanity.com/paper/2307.04954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction](https://mlsanity.com/paper/1803.01254/) (2018)
- [Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values](https://mlsanity.com/paper/2005.11627/) (2020)
- [Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models](https://mlsanity.com/paper/1606.05320/) (2016)
- [Improving the Performance of the LSTM and HMM Model via Hybridization](https://mlsanity.com/paper/1907.04670/) (2019)

## Built on by (most cited first, 3 of 3)

- [MSTIM: A MindSpore-Based Model for Traffic Flow Prediction](https://mlsanity.com/paper/2504.13576/) (2025)
- [Enhancing short-term traffic prediction by integrating trends and fluctuations with attention mechanism](https://mlsanity.com/paper/2504.19967/) (2025)
- [Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction](https://mlsanity.com/paper/2605.00083/) (2026)

## Abstract (arXiv)

Deep learning (DL) methods have outperformed parametric models such as historical average, ARIMA and variants in predicting traffic variables into short and near-short future, that are critical for traffic management. Specifically, recurrent neural network (RNN) and its variants (e.g. long short-term memory) are designed to retain long-term temporal correlations and therefore are suitable for modeling sequences. However, multi-regime models assume the traffic system to evolve through multiple states (say, free-flow, congestion in traffic) with distinct characteristics, and hence, separate models are trained to characterize the traffic dynamics within each regime. For instance, Markov-switching models with a hidden Markov model (HMM) for regime identification is capable of capturing complex dynamic patterns and non-stationarity. Interestingly, both HMM and LSTM can be used for modeling an observation sequence from a set of latent or, hidden state variables. In LSTM, the latent variable is computed in a deterministic manner from the current observation and the previous latent variable, while, in HMM, the set of latent variables is a Markov chain. Inspired by research in natural language processing, a hybrid hidden Markov-LSTM model that is capable of learning complementary features in traffic data is proposed for traffic flow prediction. Results indicate significant performance gains in using hybrid architecture compared to conventional methods such as Markov switching ARIMA and LSTM.