  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.
