Students are usually unaware of their own skills. They choose to follow the trend, rather than the proper pathway. Which negatively affects the professional sector, and the development of the country. Orienting students, and guiding them would offer considerable benefits. Building the appropriate student's profiles is the golden key for an accurate orientation. To do so, relying on the simple use of the grade point average (GPA) will not be sufficient, and mislead the guidance. Instead, studying their personality and skills has to be done, in order to provide them with their reel orientation. The presented solution aims to orient students to the most suitable career, based on a mathematical model, valid for all education systems, and takes into account the trades trends and students capabilities.

Traffic congestion is being experienced daily by many travellers both on the inner-city roads as well as on the highways. The toll plaza can become a real highway bottleneck, as vehicles must stop and manually pay the tolls at the toll booths. This stop-and-go process can seriously affect traffic fluidity, resulting in a lower quality of service at the plaza. One of the solutions used to improve the efficiency of toll plazas operation was the implementation of electronic toll collection (ETC), where vehicles can pass through the tolls without having to stop. In this research, a model that evaluates the benefits of the ETC system was developed using ARENA, a microscopic simulation tool. These benefits were visualized by conducting a comparative analysis of different scenarios. The Bouznika toll plaza, one of the major toll highways in Morocco, was selected for illustrating the improved operation realized from the use of ETC technology.

There is no doubt that a good knowledge of traffic demand has a direct impact on improving traffic management. Road traffic is strongly correlated with many factors such as day of week, time of day, season and holidays which make it suitable for prediction. In this paper, we develop a neural network model for hourly traffic prediction that makes full use of these temporal characteristics. The proposed algorithm is tested on a real-world case, and the experiment results is presented to evaluate its accuracy.