## 1 Introduction and Related Work
From the literature, we can mention some papers that study the implementation of a neural network on microcontrollers such as the ARM microcontroller [8; 9] and address the problem of finite precision of such microcontrollers [10]. Neural networks based on graphs are successfully used in traffic prediction, traffic congestion, traffic surveillance, and even in the assisted driving of vehicles. Based on these networks, traffic signal control systems, traffic incident control systems, and optimal route prediction can be created. The data sets used as inputs in neural network (the information from the graph nodes that model the transport network) can be taken from sensors placed on the road, from GPS systems, or from specific applications that monitor vehicles (for example, in public transport). The graphs can be represented either as directed graphs (the direction of travel of the vehicles is considered) or as non-oriented graphs (ignoring the direction of travel of the vehicles). The representation mode of the graph influences the performance of the neural network, but from the perspective of its real-time implementation, a simpler but effective representation mode must be chosen. Most works indicate a graph representation using the Laplacian matrix and a recurrent neural network (usually LSTM) [11; 12]. The input data set can be coded in such a way as to include external factors (for example, weather conditions) [13; 14]. However, increasing the complexity of the input data set will increase both the training time and the prediction time in the testing phase. In this paper, we considered a simplified set of input data, the traffic load, which is taken either from sensors (real traffic) or from the output of the neural network (predicted traffic). Starting from these choices, this paper creates a framework for the real-time implementation of a neural network based on graphs and evaluates its performance in terms of prediction time and prediction accuracy under the conditions of using limited resources. The following aspects are addressed in this paper:  
* What type of neural network should be chosen so that the implementation can be carried out on microcontrollers (with relatively limited resources);
* What is the number of graphs at the input so that the prediction is as good as possible (the maximum number of time points);
* How the graph can be represented to improve the neural network performance;
* Evaluation of computing time and prediction accuracy. The main issues encountered are low power consumption, numerical representation, memory requirements, and execution time. On the other hand, the embedded system should be able to run all the necessary tasks required by the above-mentioned applications, i.e., acquire the data sensors, test the neural network, decide, and transmit it to a server for monitoring and to take further actions  
values can be different from the real traffic values. A compromise must be made between the load of the communication network connecting the vehicles, the application server, and the database server and the accuracy of the prediction. In this work, we consider scenarios in which the vehicles transiting roads request information about the traffic load and can predict the traffic to choose a route as short as possible to decrease road congestion, the travel time, and amount of energy consumed [1; 16]. This paper presents cases derived from the point of view of the reading mode of sensors according to the models found in real-time systems: time-triggered and event-triggered. These scenarios evaluate the load of the communication network connecting the system components (especially vehicles and sensors) to obtain a compromise between a reduced load of the communication network and the accuracy of the prediction. Spatial-temporal interactions are captured by using graphs as reinforcements in a recurrent neural network.