# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 2 Literature Review
[17] first extracted the data of several typical road traffic states, then constructed a running feature sequence between the roads, constructed the kernel function of the time series to select the nearest road traffic states for prediction, and then used a selection of actual traffic data in Beijing to predict the traffic flow. The kernel function of the KNN model proposed in this paper is feasible and has high accuracy. Through analysis, it was found that when extracting the spatial featuresof the data, this study simply used the connection between the roads as the spatial feature and did not deeply mine the spatial features of the data itself, so it could be improved in this aspect. [PERSON] et al. [18] proposed an adaptive K-nearest neighbor model (adaptive STKNN) for traffic flow prediction, given the fixedness and uncertainty of spatiotemporal dependence in current traffic flow prediction models. Firstly, the size of the spatial field was calculated by the cross-correlation function to extract the spatial dependence between the roads. Secondly, the autocorrelation function was used to determine the time window length of the time series. Finally, the space-time weight was introduced into the distance function to optimize the search mechanism of KNN. The dataset of highways in California, USA was verified, and the comparison with the four traditional models showed that the modified model has better prediction performance. A key contribution of this study is showing the spatial heterogeneity of urban transportation networks, that is, the spatial dependence of traffic flow data. However, the problems existing in the KNN model itself cannot be ignored. First, the calculation amount of KNN is relatively large. When the amount of data and feature points increases, the KNN algorithm is unsuitable. Secondly, the interpretability is not strong. During the screening, due to its characteristics, it selects the features with many similar surrounding features, forcibly assimilates other data features, and cannot fully mine the unique features of the data itself. [PERSON] [10] proposed an online regression support vector machine (OL-SVR) model and employed supervised learning techniques to forecast traffic flow under atypical and typical circumstances, which attained suitable results. Recently, various methods for deep learning have been commonly used for traffic flow prediction, and deep learning is advantageous in autonomously learning the complexity, randomness, and hierarchical features of traffic data [19]. Initially, [PERSON] et al. [20; 21] advanced a fast-learning algorithm on account of DBN and further improved it. Based on this, [PERSON] et al. [22] pioneered the application of DBN in short-term traffic flow forecasting and categorized the deep structure into the DBN at the bottom layer, which performs unsupervised learning of traffic flow features, as well as the multitask regression layer at the top that performs supervised prediction. Using large amounts of unlabeled data to learn features, labeled data for supervised adjustment, and then training on real datasets, the results showed the promise of deep learning in the transportation domain. However, although the DBN network shows greater advantages in processing complex data, because there is no connection between units in the network, the learning process is slow in the training process, and improper parameter selection can easily lead to local maximum superior issues. Moreover, [PERSON] et al. [23] advanced an improved SAE on account of the greedy layer-by-layer training method. By stacking the autoencoder layer by layer, the data are input into the autoencoder for encoding and decoding, so that the dimensions of the input data and the output data are consistent, the data features are preserved to the greatest extent, and redundant information is reduced. Its structure is similar to that of DBN, both of which are unsupervised learning by stacking network layers. [PERSON] et al.