# Semantic Traffic Sensor Data:
## 2 Related Work
### Analysis of Traffic-Related Ontologies
Regarding the representation of traffic sensor data, the focus is only on the traffic flow, and, rather than proposing a new ontology or extending an existing one, the SSN ontology is directly adopted. * The Open511 specification [31] has been proposed as an open format for publishing road event data. Information about the road events can be provided by publishing an XML file or by allowing access to the data through a dynamic API. It supports representing elements such as events and geographic areas (places represented in GeoNames [32; 33]); examples of events are constructions, special events (such as the celebration of a sport event), incidents (including accidents and other unexpected events), weather conditions, and road conditions (such as snow, ice, or fire on the road). This work currently covers event data rather than traffic information. Nevertheless, some additional resources have also been proposed (currently as drafts that may be included in the Open511 specification in the future) to represent average historical speeds and the current speed of road segments. * The Road Accident Ontology [34] focuses on the representation of information about accidents (vehicles affected, location of the accident) and the parties involved (persons involved in the accident and their insurance companies). This proposal is a draft, submitted by [PERSON] for the W3C Geek Week celebrated in July 2012. This ontology does not represent traffic, but we have included it because accidents can affect traffic and even lead to traffic jams. * As another work focusing on accidents, the work in [35] proposes a lightweight Car Accident Ontology for VANETs (CAOVA), that includes information about vehicles, accidents, occupants and the environment. The goal is to facilitate information about an accident to emergency vehicles. * It is also relevant to mention the Transportation Planning Suite of Ontologies (TPSO) [36], which is a set of ontologies proposed for transportation planning. More specifically, eight ontologies are proposed to cover concepts related to time, meteorology, spatial locations, units of measure, changes, activities, recurring events, resources, and observations. Among these, we can highlight here the Observation Ontology [37], which reuses the SSN Ontology to capture the concepts related to sensors, but also extends it by adding a few classes and properties for the organization of terms. Specific traffic properties (such as the traffic flow or speed) are not explicitly modeled in the proposed ontology. * The KM4 City [38] is an ontology for smart cities developed by the University of Florence (Italy) as a support for a platform that collects and integrates data related to the Tuscany region in Italy. It includes concepts regarding streets (Road, Node, RoadElement, AdministrativeRoad, Milestone, StreetNumber, RoadLink, Junction, Entry, EntryRule, Maneuver, Lanes, and Restriction), local public transportation (Ride, Route, RouteSection, BusStop, etc. ), and sensors of traffic and different types of events (e.g., SensorSite, TrafficObservation, TrafficSpeed, TrafficConcentration, TrafficHeadway, etc.). * Finally, some ontologies support modeling energy consumption data. Although they are not explicitly focused on traffic, they could be used as an input for traffic estimation. On the one hand, the Smart Appliances REFerence (SAREF) ontology [39] allows the representation of information related to devices (e.g., a washing machine, a temperature sensor, etc.) in a smart appliances domain as well as their functions and profiles (e.g., for energy optimization). On the other hand, the FIEMSER ontology [40] models the organization of building spaces (using concepts such as Building, BuildingPartition, BuildingSpace, and BuildingZone) and the devices used in the building (defining concepts such as Device, HomeEquipment, ControlledDevice, and also more specific types such as Boiler and Radiator).