Harnessing the potential of new generation transport data and increasing public participation are high on the agenda for transport stakeholders and the broader community. The initial phase in the program of research reported here proposed a framework for mining transport-related information from social media, demonstrated and evaluated it using transport-related tweets associated with three football matches as case studies. The goal of this paper is to extend and complement the previous published studies. It reports an extended analysis of the research results, highlighting and elaborating the challenges that need to be addressed before a large-scale application of the framework can take place. The focus is specifically on the automatic harvesting of relevant, valuable information from Twitter. The results from automatically mining transport related messages in two scenarios are presented i.e. with a small-scale labelled dataset and with a large-scale dataset of 3.7 m tweets. Tweets authored by individuals that mention a need for transport, express an opinion about transport services or report an event, with respect to different transport modes, were mined. The challenges faced in automatically analysing Twitter messages, written in Twitter's specific language, are illustrated. The results presented show a strong degree of success in the identification of transport related tweets, with similar success in identifying tweets that expressed an opinion about transport services. The identification of tweets that expressed a need for transport services or reported an event was more challenging, a finding mirrored during the human based message annotation process. Overall, the results demonstrate the potential of automatic extraction of valuable information from tweets while pointing to areas where challenges were encountered and additional research is needed. The impact of a successful solution to these challenges (thereby creating efficient harvesting systems) would be to enable travellers to participate more effectively in the improvement of transport service

The Brenner is the most heavily travelled transalpine corridor in terms of freight transport. The current modal split tends heavily towards road (71% road – 29% rail), with significant repercussions in terms of environmental and social impacts. Indeed, Alpine areas generate external costs that are up to four times higher than flat areas. The promotion of railway, which is the least impacting transport mode, has thus a strategic value. For this reason, the European Union, the Alpine macro-region and the Tyrol-South Tyrol-Trentino Euroregion are promoting multi-level strategies to reduce the impact generated by freight transport along the Brenner corridor. This paper analyses each level, focussing then on the Euroregion. Its ambitious objective is to achieve a balance between transport modes in the Alpine corridor by 2027, and then to invert them (29% road – 71% rail) by 2035. This paper aims to calculate the savings of external costs deriving from the achievement of this objective. If the aggregated data from 2015 to 2035 are considered and the Euroregion scenario is compared with a prosecution of the current trend, the saving in external costs would amount to €262 M (− 26% than the current trend). This value is not negligible; being equal to 4.7% of the annual public expenditure incurred by the Autonomous Province of Bolzano, it suggests to policy makers the importance of taking up measures to encourage the modal shift.