The recent advancements in the Internet of Things (IoT) have facilitated the deployment of numerous applications that enhance urban intelligence by improving the monitoring and control of city assets, such as lighting systems, traffic signals, and public transportation. However, these applications are often tailored to the specific needs of individual cities, limiting their replicability in other locations primarily due to the lack of interoperability in communication signals between assets. Despite various initiatives to establish standards for interoperability, real-world implementations frequently fall short of achieving fully interoperable systems that can be universally replicated, largely due to the absence of comprehensive solutions and implementation examples. This paper presents a fully interoperable use case for Green Mobility solutions in a smart city, utilizing air quality, traffic, and noise intensity data to provide transport recommendations to end-users. The implementation employs the NGSI-LD standard, Fiware data storage tools, and developed artificial intelligence-based algorithms to predict the transport situation for the following day and offer relevant traffic recommendations. This work has resulted in the development of several data models and standardized forecast algorithms with accuracy exceeding 75% on the noise and traffic datasets at our disposal, thereby enabling the potential for replication in other locations.