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.

翻译：深度学习方法在短时及近短时交通变量预测中已超越历史平均、ARIMA及其变体等参数模型，这些预测对交通管理至关重要。具体而言，循环神经网络（RNN）及其变体（如长短期记忆网络）专为保留长期时间相关性而设计，因此适用于序列建模。然而，多机制模型假设交通系统会经历多个具有不同特征的状态（例如自由流、拥堵状态），并针对每个机制训练独立的模型以刻画交通动态。例如，基于隐马尔可夫模型（HMM）进行机制识别的马尔可夫切换模型能够捕捉复杂的动态模式和非平稳性。值得注意的是，HMM和LSTM均可用于从一组潜在或隐状态变量对观测序列进行建模。在LSTM中，隐变量通过当前观测值和前一隐变量以确定方式计算；而在HMM中，隐变量集合构成一条马尔可夫链。受自然语言处理研究启发，本文提出一种混合隐马尔可夫-LSTM模型，该模型能够学习交通数据中的互补特征，用于交通流预测。结果表明，与马尔可夫切换ARIMA和LSTM等传统方法相比，采用混合架构可显著提升性能。