## 1 Introduction and Related Work  
Currently, neural networks can be implemented on embedded systems for speech recognition, object detection, human activity recognition, time series forecasting, etc. Traffic prediction is currently important due to the large number of vehicles that transit road networks. Increased accuracy in predicting traffic will lead to the most efficient use of transport networks and help make decisions to increase the transport capacity on very congested routes. Also, traffic prediction shortens the transport times between two points by choosing a less-congested route. Terrestrial transport networks are modeled as graphs with destinations (cities) as nodes (vertices). Each node can have several edges representing the paths between cities. The nodes contain information about the number of vehicles transiting the coverage area of the sensor associated with that node [1]. Transport networks can be dynamic and have complex dependencies. This means that spatial-temporal aspects must be considered. For a graph representation, each node changes its traffic values over time. Traffic forecasting can be accomplished using a neural network that considers both spatial characteristics (the way the nodes are connected) and temporal characteristics (the modification of the traffic values in the node over time). The inputs to the neural network are represented by graphs at different time points. The output of the neural network is a graph that has predicted information (the number of vehicles and their speed) in nodes and edges. One of the impactful applications of graph-based neural networks (GNN) is in improving the accuracy of estimated arrival time by learning the structure and dynamics of a transport network through the neural network. Using such neural networks, future traffic prediction algorithms can be created (used, for example, by Google Maps) [2]. Using GNN is a challenge due to spatial-temporal complexity, but there are advantages in relation to other classical neural networks (for example, convolutional neural networks that cannot represent the topological structure of a transport network). For traffic prediction, a graph built according to the topology of a transport network is naturally used. The main problems that can be seen on this graph include the number and type of vehicles on the road, their speed, road events, etc. [3]. Another important problem studied in connection with the use of graphs in neural networks refers to how they are represented. Basic methods of graph representation (the adjacency matrix, neighbor matrix, distance matrix, and Laplacian matrix) are shown in [3]. There are graph transformation methods for better scalability, such as GraphGPS, as described in [4], and TokenGT, as detailed in [5]. Code examples in high-level programming languages that implement a neural network based on graphs are illustrated in [3]. The use of microcontrollers for the implementation of neural networks (testing phase) must consider the memory requirement, processing accuracy, and execution time. The continuous development of microcontroller architectures makes such implementations viable, but further research is still needed, especially for neural networks with a higher degree of complexity [6]. Data compression methods used to reduce memory requirements are illustrated in [7]. Some classical neural network architectures (the recognition of human activity and image processing) have already been ported to microcontrollers [7]. However, implementation problems in devices with limited resources (not powerful computing systems) are not addressed very much in the literature. In the present work, the possibility of realizing an implementation on microcontrollers is studied, and the performances are analyzed. The main purpose of the article is to evaluate if neural network implementation (testing phase) is viable under the conditions of using limited resources (such as memory, calculation speed, and numerical precision) that a microcontroller has.