## 1 Introduction  
Traffic state prediction is a crucial component of an intelligent transportation system (ITS), which facilitates vehicle mobility, reduction of traffic congestion, and boosting of the economy. The transportation sector of developed countries accounts for 6 to 25% of their gross domestic products (GDP). On a macroeconomic level for advanced economies, it can account for up to half of their GDP [1]. These figures reflect the importance of efficient ITS implementation, which motivated the current research study. Here, the traffic state prediction problem is modelled as a time series problem, where the prediction of future traffic states depends on previous traffic state data, such as speed and flow from an observed road section at different periods. Numerous previously suggested approaches have shown potential to solve this problem. Data-driven strategies are the most commonly employed techniques to address it because of their capabilities for dealing with complicated route layouts and the non-linearity of traffic flow [2]. However, corrupted and missing data samples pose difficulties in getting good estimation results using these approaches. A model that quickly and efficiently acquires the hidden featuresand spatio-temporal correlations in the data can have excellent performance. Additionally, the prediction of future states is always dependent on the previous states. Hence, the long short-term memory (LSTM) and gated recurrent unit (GRU) models, because of their worthiness in handling sequential information flows and internal memory, are remarkably suited to overcome these difficulties [3]. Convolution neural networks (CNNs), which are state-of-the-art, for example, in image processing and very efficient in extracting hidden features from input data, could be further combined with LSTM or GRU models in order to enhance the performance of the traffic state prediction system. However, traffic state data is one dimensional (1D) in nature, instead of an image that is two (or three) dimensional, making it difficult to apply to a CNN. The solution is 1D-CNN, where the kernel moves in one direction and can be used to extract features from the traffic state data, as can be seen in Figure 1.  
In [4; 5; 6], CNN- and LSTM-based models were proposed to solve traffic state prediction problems. In [4], a deep temporal convolution network using residual connections for short-term traffic flow forecasting tasks was presented. The authors introduced a hierarchical temporal convolutional filter structure to capture the long-range dependencies using one-year-long datasets from road side units (RSUs) in the United Kingdom ([[https://webtris.highwaysengland.co.uk/](https://webtris.highwaysengland.co.uk/)]([https://webtris.highwaysengland.co.uk/](https://webtris.highwaysengland.co.uk/)) (accessed on 13 April 2022)) and achieved a mean absolute error (MAE) of 8.425. In [5], a combined CNN and LSTM architecture was proposed for multi-lane traffic state prediction for the PeMS datasets ([[https://pems.dot.ca.gov/](https://pems.dot.ca.gov/)]([https://pems.dot.ca.gov/](https://pems.dot.ca.gov/)), Caltrans Performance Measurement System (PeMS) accessed on 13 April 2022)) by applying multiple features, mainly average speed and flow, for traffic state characterisation and by considering routing information, which led to an average MAE of 6.732 and 1.475 for flow and speed prediction, respectively. On the other hand, in [6], a vector auto-regression model was used prior to the CNN-LSTM architecture in order to capture the intrinsic association between different traffic variables. Based on the Yanan urban expressway and Shanghai datasets [7], the results led to an MAE of only 0.396. The current study proposes a model that differs from the aforementioned models in terms of architecture: three cells of stacked 1D-CNN and 1D-MaxPooling layers are applied to capture the hidden features, and three LSTM cells are used for predicting future traffic states taking into account the mean squared logarithmic error as the loss function.