Advanced pervasive transportation services aim to improve the safety and efficiency of public and private transportation facilities, while reducing operating costs and improving the travel experience for drivers, passengers and other travellers. In order to achieve these goals, such services require access to context information from a myriad of distributed, heterogeneous Intelligent Transportation Systems. A context management scheme that models information in a standard fashion is essential to support information sharing between individual systems, and higher-level information reasoning. This paper presents an ontology-based spatial context model, which takes a combined approach to modelling context information utilised by pervasive transportation services: the Primary-Context Model facilitates interoperation across independent Intelligent Transportation Systems, whereas the Primary-Context Ontology enables pervasive transportation services to reason about shared context information and to react accordingly. The independently defined, distributed information is correlated based on its primary-context: location, time, identity, and quality of service. The Primary-Context Model and Ontology have been evaluated by modelling a car park system for a smart parking space locator service.