The potential of social media in delivering transport policy goals

Information flow plays a central role in the development of transport policy, transport planning and the effective operation of the transport system. The recent upsurge in web enabled and pervasive technologies offer the opportunity of a new route for dynamic information flow that captures the views, needs and experiences of the travelling public in a timely and direct fashion through social media text posts. To date there is little published research, however, on how to realize this opportunity for the sector by capturing and analysing the text data. This paper provides an overview of the different categories of social media, the characteristics of its content and how these characteristics are reflected in transport-related posts. The research described in this paper includes a formulation of the goals for harvesting transport-related information from social media, the hypotheses to be tested to demonstrate that such information can provide valuable input to transport policy development or delivery and the challenges this involves. A hierarchical approach for categorizing transport-related information harvested from social media is presented. An explanatory study was designed, based on the understanding of the nature of social media content, the goals in harvesting it for transport planning and management purposes and existing text mining techniques. An exploratory case study is used to illustrate the process based on Twitter posts associated with particular UK sporting fixtures (i.e. football matches). The results demonstrate both the volume and pertinence of the information obtained. Whilst text-mining techniques have been applied in a number of other sectors (notably entertainment, business and the political arena), the use of information in the transport sector has some unique features that stem from both day-to-day operational practices and the longer term decision making processes surrounding the transport system – hence the significance and novelty of the results reported here. Many challenges in refining the methodology and techniques remain for future research, however the outcomes presented here are of relevance to a wide range of stakeholders in the transport and text mining fields. •Social media has valuable information for transport policy that can be harvested.•A schema to identify and categorize social media transport information is presented.•Automatic classification gives high performance (in terms of Precision and Recall).•Almost half th