This demo paper describes a real-time passenger information system based on citizen sensing and linked data. Real-time passenger information (RTPI) systems provide details about public transport, allowing passengers to plan and make decisions regarding their journeys. Typical requirements for RTPI systems include: 1) listing available public transport services; 2) providing timetable (schedule) information for those services; 3) providing (real-time) vehicle locations; and 4) providing details of disruptions. However, few RTPI systems exist in rural areas for a variety of reasons, including a lack of infrastructure for obtaining and providing real-time information [7]. As part of the Informed Rural Passenger project1, we are developing GetThere, an RTPI system for rural areas. The GetThere system consists of a smartphone app, supported by a semantic infrastructure that integrates data from multiple sources (including users). This system has been deployed in the Scottish Borders, UK in partnership with First Group. This demonstration2 will show a typical use of the GetThere app to view timetabled and real-time vehicle locations for a selected route, contribute vehicle locations while making a journey, report a disruption event, and assess the quality of real-time locations with and without the presence of disruption. The demo will utilises the datasets and services shown in Fig. 1.
Introduction
GetThere is supported by a semantic information ecosystem (Fig. 1) itself
underpinned by a series of ontologies. Semantic web and linked data technologies are
1 http://www.dotrural.ac.uk/irp
2 A video of the demo is available at http://www.gettherebus.com/iswcdemo/
used for data representation and storage within the ecosystem as they provide
an e ective approach for large scale data integration [
Android Mobile Application User
Sensor Location Observation
Disruption
Quality Provenance Infrastructure
User Observation
Disruption
External Datasets
NaPTAN Clients Web Services Timetable Internal Datasets Timetable Ontologies
Details of public transport services and timetables are stored in the Timetable dataset3 and represented by the Transit ontology4. This dataset is used by the Timetable Service to provide details of available transport services, timetable, and vehicle location information to the GetThere app. The Infrastructure dataset provides details of the road networks used by public transport vehicles. This data is extracted from openstreetmap.org, and is represented using the Infrastructure ontology5 (which de nes bus route maps) and the LinkedGeoData6 ontology. NaPTAN7 provides details of bus stops, including their IDs and locations.
The User ontology8 and dataset describe user pro les using SIOC9 and
FOAF10, a description of each user's msobile device(s), along with details of
public transport journeys made while using the GetThere app. The Observation
dataset uses the Transport Sensors ontology11 which extends the W3C
Semantic Sensor Network (SSN) ontology12 to describe observations (e.g. of vehicle
occupancy level, vehicle location) obtained from users of the GetThere app. The
Sensor service provides an API for storing and retrieving sensor and observation
3 Timetable information is received in the ATCO-CIF format (http://www.
travelinedata.org.uk/CIF/atco-cif-spec.pdf); the RDF conversion program is
available at https://github.com/dcorsar/ecosystem.timetable.
4 http://vocab.org/transit/terms/
5 http://www.dotrural.ac.uk/irp/uploads/ontologies/infrastructure.owl
6 http://linkedgeodata.org
7 http://data.gov.uk/dataset/naptan
8 http://www.dotrural.ac.uk/irp/uploads/ontologies/user.owl
9 http://rdfs.org/sioc/spec/
10 http://xmlns.com/foaf/spec/
11 http://www.dotrural.ac.uk/irp/uploads/ontologies/sensors.owl
12 http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
descriptions expressed using the SSN ontology; the Location Observation
service handles real-time locations provided by app users. The Travel Disruption
ontology describes di erent types of disruption, based on an analysis of existing
travel disruption information sources [
Given the open nature of this data, issues such as data quality and trust
naturally arise [
Our current quality metrics are focused on real-time locations, and have been developed following several deployments of the system. They include: Timeliness - timely observations are under 1 minute old; Accuracy - accurate observations have a GPS error margin of less than 25 metres; Relevance - relevant observations are no further than 500 metres from the expected route of travel; Availability observations with a high availability score have no more than a 1 minute delay between being created on the device and published by the ecosystem.
The provenance service uses the W3C Prov-O ontology14 to maintain a record
of the entities, agents, and activities involved in producing data within the
ecosystem. Uses of provenance include: associating users with location
observations generated by their mobile device, which can support detection of
potentially malicious users; and recording dataset provenance to ensure the latest
timetable information is provided to users [
The GetThere Smartphone App The ecosystem has been designed to support a range of applications through the creation of relevant application services. At present we have used the ecosystem to support the GetThere RTPI system, which is provided via an Android smartphone app (see Fig. 2). The app invokes the web services, which execute SPARQL queries against relevant datasets, process the results, and send a response to the app. Users are presented with a list of available bus routes; after selecting a route (and direction, either inbound or outbound), vehicle locations are displayed. These locations include both estimates based on the timetable and real-time locations obtained from other users on that route (Fig. 2, left screenshot). Bus stops along the route are also shown. The user can access timetable information for the previous and next arrivals at a particular stop. When the user boards the bus, they have the option of pressing a button to have their 13 http://sensornet.abdn.ac.uk/onts/Qual-O.ttl 14 http://www.w3.org/TR/prov-o/ location uploaded to the ecosystem every minute. The uploaded location is then used as the vehicle's real-time location provided to other users.
Users can view quality assessment results for a real-time vehicle location by tapping its icon. We are working with users to determine an appropriate visualisation of quality results. Currently each assessed dimension is shown with a colour-coded bar representing its quality score (Fig. 2, centre screenshot). Acknowledgements The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1