## 2 Related Works
### Convolution Neural Network  
A combination of CNN and LSTM or GRU models can also solve traffic state prediction problems. Based on taxi big data, in [23], a model using spatio-temporal trajectory topology for traffic congestion prediction was presented. They used CNN to extract the spatial characteristics, and the LSTM memory characteristics were employed to recover the temporal characteristics from the trajectory of traffic flow. The experimental results showed a 1-2% improvement over conventional state-of-the-art techniques. However, the achieved MAPE of 24.339% indicates that the proposed model failed to provide efficient prediction accuracy. Hence, the current study aims to improve the prediction performance by offering a new architecture without using trajectory topological map extraction.  
A differential approach proposed to reconstruct unstable time series into stationary ones and then feed them to a CNN-LSTM-based model demonstrated increased traffic flow prediction accuracy. The proposed model exhibited a MAPE of 76.14% for a 60 min prediction horizon [24]. Although prediction over a more extended period is more challenging, the presented accuracy is lower than usual. Thus, the model proposed in this article intends to increase prediction accuracy by applying a different approach that does not require reconstruction of the time-series dataset.  
For traffic congestion prediction, a combination of a CNN with a bi-directional LSTM exhibited its worthiness. The CNN and Bi-LSTM models captured the spatial and temporal features, respectively. The initial stage involves folding traffic speed data according to spatio-temporal characteristics and building a three-dimensional matrix for the final model's input. Compared to traditional and state-of-the-art approaches, the results showed a higher prediction accuracy with a mean absolute error of 0.43 [25]. However, only the data of 30 weekdays was used, which resulted in a lack of robustness. Traffic patterns are entirely different on weekends and weekdays. Hence, a model trained only on weekday data always lacks robustness and efficiency. Therefore, the current study took into account both weekday and weekend data to address this drawback.  
In [26], a non-linear and non-periodic traffic speed prediction model was proposed based on a CNN for recognising spatio-temporal patterns of possibly congested urban traffic, which improved the accuracy by up to 23.8% for specific road segments. A cone-shaped binary masking approach selected the relevant input features to achieve good results. However, the prediction was for a 20 min time horizon, and this study aimed to extend the prediction time horizon with excellent accuracy.  
The use of an inception-CNN-based traffic speed prediction model, where asymmetric convolution kernels were used to extract spatio-temporal correlations by focusing on different time or space domains, also exhibited improvements in both robustness and accuracy [27]. Such an approach enhanced the model's non-linear representability and achieved an MAE of 2.45 for 30 min time steps on the Yanan urban expressway, Shanghai datasets. However, it required 8.3 million parameters for training and achieved an MAE of 2.55 in prediction over a 60 min time horizon. This study aimed to achieve better MAE values with fewer parameters.  
Hence, all the aforementioned models need performance improvement in terms of robustness and accuracy. Notably, for prediction over a longer time horizon, tremendous efforts are required to meet the expectations. Fortunately, this study shows that efficient prediction performance can be achieved even for a longer time horizon by combining a 1D-CNN with LSTMs without using additional weather or adjacent intersection information.