# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 2 Literature Review  
In this section, we primarily discuss the existing literature on parametric, nonparametric, and hybrid models that have been applied to traffic flow forecasting. In a general way, the parameter model based on time series prediction is widely used. [PERSON] et al. [11] applied the time series model to short-term electricity price forecasting. By proposing a P-order function autoregressive model, the final prediction error (FFPE) in the function was used to independently select the dimension and lag structure of the model, the example data were used for prediction evaluation, and the results showed that the method has superiority in prediction performance. [PERSON] et al. [12] further predicted short-term electricity demand by proposing function autoregression (FAR), FAR with exogenous variables (FARX) and the classical univariate AR model, and further verified the data through Nord Pool electricity market data. The FARX model is more superior in training prediction. In 1979, [PERSON] et al. [13] first proposed the application of the ARIMA model to study expressway traffic flow. Subsequently, numerous variant models have been put forward to heighten the prediction ability of the ARIMA model. In this context, [PERSON] et al. [4] came up with a hybrid model integrating the Kohonen method and ARIMA method to forecast short-term traffic flow, which enhanced prediction accuracy by simplifying the problem of class definition. [PERSON] et al. [14] developed a comprehensive and multiplicative seasonal ARIMA model for forecasting the traffic sequence based on its characteristics. Thereafter, [PERSON] et al. [6] advanced a model focused on the data available to satisfy the requirement of the prior models in terms of a sound database. Accordingly, they proposed a seasonal ARIMA (SARIMA) model to resolve this limitation. [PERSON] et al. [15] designed a unified spatiotemporal model on account of spatiotemporal ARIMA (STARIMA). The prediction period parameters of the model can be conveniently modified to adapt to the varying complex road structure, and the spatiotemporal characteristics between roads can be captured to improve the degree of the preciseness of traffic flow forecasts. The above-mentioned variant models resolved the problems that could not be solved in the previous ARIMA models, such as the low prediction accuracy caused by the inability to manipulate nonlinear data and the randomness of traffic data. However, both the ARIMA model and the variant model have the same problem; that is, the structure of the model itself is relatively simple, which is not suitable for processing complex data, and the model is more for processing linear data, while traffic flow data itself has complex spatial characteristics, and the time dependence is nonlinear and has large randomness and periodicity. Therefore, a more complex model structure is required for training, and the data characteristics of traffic flow are deeply excavated. In case of a surge in traffic flow data, nonparametric methods based on an extensive amount of data can more effectively train the data, and shallow machine learning models can capture the key features of traffic. [PERSON] et al. [16] established the KNN model through three aspects: temporal data, parameters, and a prediction mechanism. First, by standardizing the initial data, the magnitude difference between the data samples was reduced to improve the prediction accuracy. Using the traffic flow data of the fast road section in Shanghai, and comparing and analyzing different model algorithms, the results showed how the KNN model proposed in this paper could be used. In regards to its feasibility in predicting short-term traffic flow, however, the spatiotemporal correlation of traffic flow was seriously neglected in this study. [PERSON] et al.