# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
## 2 Literature Review  
Only a few studies in short-term traffic prediction from the last few decades elaborated network-wide traffic prediction. Most of the approaches engrossed arterial or corridor levels. To increase the accuracy and efficiency of traffic forecasting, numerous studies have been proposed.  
On the whole, these approaches are grouped as parametric and nonparametric approaches. In many traffic prediction practices, parametric approaches leverage counts in time series and linear regressive models. To identify the primary travel time pattern, [PERSON] et al. [8] proposed a Bayesian dynamic linear model to predict accurate travel time. To characterize the temporal travel time evolution, many entropy-based measures were proposed by [PERSON] and [PERSON] [9]. [PERSON] et al. [10] envisaged expressway traffic flow by applying the ARIMA model. When the traffic conditions are regular, parametric approaches are very appropriate to use. However, as traffic data involve nonlinear and stochastic properties, the performance of parametric approaches becomes undesirable. Therefore, applying the techniques mentioned above make traffic prediction unreliable.  
Regarding nonparametric approaches, also known as data-driven models, during the traffic speed prediction, [PERSON] et al. [11] proposed the KNN model to depict the spatiotemporal correlation. [PERSON] et al. [12] proposed a short-term traffic flow model based on SVM to predict multistep traffic flow prediction. When traffic conditions are large in scale and complex with intersections and loops, upstream and downstream both refer to relative positions, and two arbitrarily locations can be downstream and upstream. The upstream and downstream are defined with respect to space, while forward and backward dependencies are defined with respect to time.  
When traffic conditions become large in scale and complex, the parametric and nonparametric approaches do not work efficiently. Deep-learning-based approaches are significantly used in traffic prediction due to their great ability to deal with spatiotemporal patterns. For traffic forecasting problems, many neural-network-based methods, e.g., feed-forward NN [13], fuzzy NN [14], recurrent NN [15], hybrid NN [16], and Gaussian process (GP) [17] have been adopted. [PERSON] et al. [18] proposed a composite road network Caps-Net architecture, which switched the max pooling task of CNN. To simultaneously predict weather conditions and traffic flow, decision level data fusion and deep belief networks were incorporated by [PERSON] et al. [19]. Transportation has a dynamic nature, which is why RNNs models are very suitable to deal with the temporal evolution of traffic status. In traffic speed prediction, long-term temporal characteristics were captured using LSTMs by [PERSON] et al. [3]. To predict travel demand in the large-scale transportation network, using stacked long short-term memory, [PERSON] et al. [20] proposed the feature-level data fusion model. [PERSON] et al. [21] incorporated a deep spatiotemporal residual network for the prediction of the citywide crowd flow by considering the period, temporal closeness, and the trend properties of human mobility. [PERSON] et al. [22] proposed a multitask model to predict network-wide traffic speed. [PERSON] et al. [7] proposed a bidirectional and unidirectional RNN-based model for the prediction of network wide traffic speed. [PERSON] et al. [23] proposed a stacked bidirectional attention-based gated recurrent unit model for large-scale traffic speed prediction.