Recent advances in smart connected vehicles and intelligent transportation systems are based on the collection and processing of massive amounts of sensor data. There are various internal sensors integrated into modern vehicles that are useful to monitor multiple mechanical and electrical systems, and the shift to semi-autonomous vehicles adds outward-facing sensors such as cameras, lidar, and radar. Low-cost, dependable sensing, connectivity, computational capacity, and powerful analytics are ushering in a new era of vehicle context sensing and vehicular network (VANET). However, due to latency, bandwidth, cost, security, and privacy issues, as well as the growing capabilities of edge computing devices, it is necessary to examine both edge and cloud computing in order to make informed judgments based on their contexts and performances. So, in this paper, we have proposed an architecture that focuses on an intelligent transportation system based on both Cloud and Edge computing in order to monitor and analyze vehicle sensor data and its environment; known as monitoring and analyzing as a service. Our proposed architecture is able to react in real-time to any vehicle context changes in a dynamic, adaptive, and autonomous way. It generates many plans adapted to the vehicle context that will be useful for future research to achieve a self-learning and self-adaptive system. This suggested architecture helped us improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. Also, we have reduced latency and increased bandwidth. Finally, we have approved the performance of our architecture based on different evaluation metrics.