David Corsar

You'll Never Ride Alone: The Role of Social Media in Supporting the Bus Passenger Experience

The paper discusses a study of social media usage within the context of a public transport operator. This involved fieldwork within three subsidiary companies of FirstGroup alongside a content analysis of the individual Twitter feeds they operate and the conversations they generate through them to engage with passengers. A refiguring of the notion of social is taking place within these companies through their emergent strategies for utilizing social media. The findings showed how the companies address this by pursuing a persistent conversation with customers, facilitating the provision of real-time information and carefully managing their Twitter identity.

Developing a real time passenger information system for rural areas

Passengers in rural areas are provided with little or no information regarding public transport disruptions. This can result in high levels of travel uncertainty with significant potential to affect travel behaviour. This paper, through 52 interviews, and 7 focus groups in rural areas in Scotland and England, explores the passenger experience, and the technology usage of individuals during disruption. The analysis indicates that a wide range of behavioural responses are evident, extending well beyond the choice of route or mode of transport. Further, we identify that the individual utilises various technologies (e.g. social media), and kinship networks to insulate against the effects of disruption. In addition, we present the co-design process of a set of technologies (a smartphone application and and an SMS service) that aim to improve the passenger exprience during disruption. This work provides an initial step towards understanding the interplay between disruption, passenger experience, and the design space for improving the passenger experience of individuals during disruption.

Reusing JessTab rules in Protégé

Protege provides a complete ontology and knowledge base management tool. Along with JESS, JessTab provides one method of rule-based reasoning over a Protege ontology and knowledge base. However, once JessTab rules have been created for a knowledge base, they are explicitly tied to it as they name particular classes and slots, which greatly hinders their reuse with further knowledge bases. We have developed a two-phase process and a supporting tool to support the reuse of JessTab rule sets. The first phase involves changing the class and slot references in the rule set into an abstract reference; the second phase involves automatically mapping between the abstract rules and further knowledge bases. Once mappings have been defined and applied for all the classes and slots in the abstract rules, the new rule set can then be run against the new knowledge base. We have satisfactorily tested our tool with several ontologies and associated rule sets; moreover, some of these tests have identified possible future improvements to the tool.

Linking Open Data and the Crowd for Real-Time Passenger Information

The availability of real-time passenger information (RTPI) is a key factor in making public transport both accessible and attractive to users. Unfortunately, rural areas often lack the infrastructure necessary to provide such information, and the cost of deploying and maintaining the required technologies outside of urban areas is seen as prohibitive. In this paper we present the GetThere system developed to overcome such issues and to provide public transport users in rural areas with RTPI. An ontological framework for representing mobility information is described, along with the Linked Data approach used to integrate heterogeneous data from multiple sources including government, transport operators, and the public. To mitigate possible issues with the veracity of this data, a quality assessment framework was developed that utilises data provenance. We also discuss our experiences working with Semantic Web technologies in this domain, and present results from both a user trial and a performance evaluation of the system.

The Transport Disruption Ontology

This paper presents the Transport Disruption ontology, a formal framework for modelling travel and transport related events that have a disruptive impact on traveller’s journeys. We discuss related models, describe how transport events and their impacts are captured, and outline use of the ontology within an interlinked repository of the travel information to support intelligent transport systems.

A linked data approach to assessing medical data

Vast amounts of medical data are now routinely collected. This data is often subsequently used in medical research. However, the quality of the data can vary widely. Existing automated approaches to data quality assurance largely rely on threshold rules that can miss errors requiring complex domain knowledge to identify. In this paper we describe a framework to assess the reliability of medical data using linked data and semantic web technologies. This approach has been evaluated in the Neuro-Intensive Care Unit domain, successfully identifying potential errors in the recorded observations, and indicating that various ontologies proposed by the medical and sensor network communities can be used to represent medical observation data.

People, Plans and Place: Understanding and Supporting Responses to Rural Public Transport Disruption

Public transport information provision in rural areas is often fragmented and of poor quality at best and non-existent at worst. This can have a significant impact on the everyday life of the inhabitants of rural areas, particularly in terms of limiting their travel choices and thereby their opportunities to access goods, service and social networks. Inadequate information provision also poses significant challenges during times of transport disruption. In this paper we examine the responses from a series of interviews (69) and focus groups (9) in which we explored the rural passengers’ experience during disruption, their coping strategies, and their behavioural responses to disruption. We identify that each passenger experiences disruption uniquely, and that the behavioral adaptation of the passenger relates to the severity and impact of the disruption. Furthermore, we identify that the most prevalent ways of mitigating the impacts of disruption is through time buffering and the use of kinship networks. Based on these findings and six co-design sessions with rural passengers we were able co-design and develop a prototype passenger information system to support the passenger during disruption. The results of this work aim to advance understandings of the interplay of technology, information provision, and passenger experience under disruption.

Utilising Provenance to Enhance Social Computation

Many online platforms employ networks of human workers to perform computational tasks that can be difficult for a machine (e.g. reporting travel disruption). Such systems have to make a range of decisions, for example, selection of suitable workers for a task. In this paper we present an approach that utilises Semantic Web technologies and provenance to support such decision-making processes.

Exploring provenance in a linked data ecosystem

We describe our work exploring provenance within an open linked data ecosystem being developed in the travel/transport domain. We discuss techniques to infer provenance of sensor data, maintain provenance of third party data, and reference sources not available as linked data within a provenance record.

DEMO: managing the provenance of crowdsourced disruption reports

Human computation systems that outsource tasks to the crowd often have to address issues associated with the quality of contributions. We are exploring the potential role of provenance to facilitate processes such as quality assessment within such systems. In this demo we present an application for managing traffic disruption reports generated by the crowd, and outline the technologies used to integrate provenance, linked data, and streams.