# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
###### Abstract  
**Problem definition:** The intelligent transportation system (ITS) plays a vital role in the construction of smart cities. For the past few years, traffic flow prediction has been a hot study topic in the field of transportation. Facing the rapid increase in the amount of traffic information, finding out how to use dynamic traffic information to accurately predict its flow has become a challenge. **Methodology**: Thus, to figure out this issue, this study put forward a multistep prediction model based on a convolutional neural network and bidirectional long short-term memory (BILSTM) model. The spatial characteristics of traffic data were considered as input of the BILSTM model to extract the time series characteristics of the traffic. **Results**: The experimental results validated that the BILSTM model improved the prediction accuracy in comparison to the support vector regression and gated recurring unit models. Furthermore, the proposed model was comparatively analyzed in terms of mean absolute error, mean absolute percentage error, and root mean square error, which were reduced by 30.4%, 32.2%, and 39.6%, respectively. **Managerial implications**: Our study provides useful insights into predicting the short-term traffic flow on highways and will improve the management of traffic flow optimization. traffic flow forecast; convolutional neural network; bidirectional long short-term memory network model; multicomponent information +
Footnote †: journal: Applied  
are mainly represented by the autoregressive integrated moving average (ARIMA) model and its varieties, e.g., [PERSON] et al. [4] used the hexagonal Kohonen self-organizing map as a classifier and stored a separate ARIMA model in each class for simplified adjustment. The separation of function approximation and classification display improved the prediction performance of the model. The seasonal autoregressive integrated moving average model (SARIMA) proposed by the literature [5; 6] uses the autocorrelation function to determine the order of the model after differencing the input time series data, and it uses the maximum likelihood estimate to determine the hyper-parameters of the model and substitute the experimental data for prediction and comparison. In addition, the Kalman filtering [7; 8] algorithm is often used for traffic flow forecasts. Nevertheless, due to the strong randomness characteristics of traffic flow data, big data training must be applied to ensure a certain accuracy, and a simple parametric model cannot be perfectly trained with big data, which would probably produce certain errors. To resolve these problems, nonparametric models should be avoided. For instance, as mentioned in the literature [9], the K-nearest neighbor (KNN) method extracts temporal features by using multistep time steps in the time series, and it inputs the extracted features into the KNN model for prediction. In view of the universality of research traffic, the literature [10] proposes traffic forecasting under accident conditions and atypical situations; that is, the data are classified into severe weather, weekends, holidays, and traffic accidents for input, and an online regression support vector machine model is used (OL-SVR) for the supervised learning of traffic features under different classifications. Although these models have improved performance compared to time series models, they also have other problems. The KNN model in the literature [9] lacks the extraction of spatial features of the data. Although the literature [10] makes traffic prediction more universal by predicting data under different classifications, it ignores the temporal correlation characteristics of the data itself and the spatial correlation between roads or between different detection points, as well as other complex characteristics, thus lacking in prediction performance.