Engineering of an urban transport recommendation system in the Democratic Republic of Congo[Ingénierie d'un système de recommandations de moyens de transport urbain en République Démocratique du Congo]

This article highlights the software engineering for the design of a recommendation system based on content and collaborative filtering for the suggestion of means of transport in an urban environment. The emergence of artificial intelligence in human daily life also affects the transport sector, thus generating intelligent innovations to the point of influencing the choice of public transport customers based on security criteria. Faced with the uncertainty of taking a safe taxi, artificial intelligence becomes a solution to help humans make a choice based on the route to take or sharing the experience of other customers on different routes. These suggestions are made by similarity metrics and Machine Learning algorithms based on class instantiable by computer applications. We use object-oriented design to present the functional architecture using UML models including use cases, the interaction model as well as the class model and the class model on recommendations. Modeling is done using unified modeling language artifacts, abbreviated UML (Unified Modeling Language). It also presents an extract of a co-occurrence matrix from the example of the TaxiReco system.