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：深度学习方法在交通变量短时及近短时预测中表现优于历史平均、ARIMA及其变体等参数化模型，这类预测对交通管理至关重要。具体而言，循环神经网络及其变体（如长短期记忆网络）专为保留长期时序相关性而设计，因此适用于序列建模。然而，多模态模型假设交通系统会经历多个具有不同特征的阶段（如自由流、拥堵状态），从而针对每个阶段训练独立模型以刻画该区间内的交通动态。例如，采用隐马尔可夫模型进行状态识别的马尔可夫切换模型能够捕捉复杂动态模式与非平稳性。有趣的是，隐马尔可夫模型与LSTM均可用于从一组潜在或隐状态变量中建立观测序列的模型。在LSTM中，隐变量通过当前观测值与前一隐变量以确定性方式计算得出；而在隐马尔可夫模型中，隐变量集合构成马尔可夫链。受自然语言处理领域研究的启发，本文提出一种能够学习交通数据互补特征的混合隐马尔可夫-LSTM模型用于流量预测。结果表明，相较于马尔可夫切换ARIMA与LSTM等传统方法，混合架构在性能上具有显著优势。