# BIM-Based Description of Intelligent Transportation Systems for Roads
## 2 Knowledge Sources for Semantic Modeling of ITS
### ITS Architecture  
Vehicular ad-hoc networks are the central paradigm relevant to ITS architectures. A vehicular ad-hoc network (VANET) is a self-forming network based on wireless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Nodes of a VANET comprise vehicles and infrastructure equipped with networking devices, connected through VANET communications [31]. The architecture of vehicular ad-hoc networks may be categorized into pure cellular or wireless local area networks, pure ad hoc networks, and hybrid networks [32]. However, intermittent and autonomous connections between network nodes may cause frequent changes in VANET topologies. Varying traffic densities, heterogeneous vehicle paths in urban areas, and limited wireless transmission ranges and channel bandwidths pose challenges to VANET communications [33]. Thus, different routing protocols and security standards are employed for vehicular ad-hoc networks to guarantee sound data packet transmissions and secure communications [34].  
Combining the VANET paradigm with the IoT in vehicular environments, i.e., the Internet of Vehicles, has led to the vehicular cloud concept, an efficient ecosystem for ITS networks [35]. The term \"autonomous vehicular cloud\" has been coined to denote a group of autonomous vehicles that share on-board resources and services with other authorized network nodes using vehicle-to-anything (V2X) communications. Therefore, by renting under-utilized vehicle resources and decentralizing data processing, vehicular clouds broaden ITS applications to non-safety location-specific applications [36].  
Vehicular cloud computing and information-centric networking are the key attributes of vehicular clouds, facilitating robust resource-sharing processes. The automotive industry continuously produces vehicles with powerful on-board resources, enabling vehicles to sense and to store data from the environment, to process the data, and to react appropriately to the data. Thus, vehicles, recording data that are locally relevant, are enabled to efficiently manage time-sensitive operations. However, the potential of computing and communication capabilities of vehicles has not been fully exploited [37]. Vehicular cloud computing (VCC) is a mechanism that combines underutilized vehicular cyber-physical system resources to perform ITS applications. In other words, VCC eliminates the need for centralized data-processing infrastructure by distributing the computing load on network nodes [38]. Information-centric networking (ICN) is a data-networking paradigm that puts emphasis on the content of data packets by decoupling the packets from node addresses [39]. Several ICN architectures and messaging protocols exist that specify standardized, machine-readable naming and beacon exchange between network nodes [40; 41].  
An example of vehicular cloud formation is shown in Figure 2, where V2X and infrastructure-to-infrastructure (I2I) communications facilitate data packet transmissions between network nodes in the corresponding vehicular clouds. Using VCC under normal conditions on the road, vehicles driving in the same direction may form vehicular clouds to process computing tasks, disseminate the information to neighboring nodes, or store and later share data packets to ITS users. ICN is of specific importance in hazardous situations, e.g., in case of accidents, where vehicles approaching a hazardous section and/or road furniture, such as traffic cameras, form intermittent vehicular clouds, disseminating alert messages and notifying traffic to avoid congestion, chain accidents, and probable casualties.  
Built upon VANET and taking advantage of VCC and ICN, vehicular clouds constitute both static clouds (e.g., in parking lots) and dynamic clouds (en route applications), hence covering a wide range of applications of ITS for roads. Consequently, in this study, vehicular clouds are considered the basic network architecture to be reflected in the semantic model.