Providing the road user information system of the future requires access to a large amount of heterogeneous data through different service providers. Since more and more organisations and public authorities provide open data, big data is an important dimension to be considered. On the one hand, data from different sources can be harnessed to augment the quality of information provided to users (and especially drivers within SIMPLI-CITY). On the other hand, open data highlights challenges in understanding different levels of data format, e.g., non-textual such as shape files.
Cities generate very large volumes of data centred around the interactions of the citizens within the city environment. Some of this data is generated by the local and government authorities and is considered as open, some of it is generated by private companies delivering services in the city (taxis, weather conditions, historical traffic conditions), and more is generated by the residents of the city (social media feeds, car incidents, road congestions, entertainment events). Within such an open and heterogeneous environment, there are opportunities for better services, less waste, and new opportunities for people to interact with the city – whether it be how to navigate the public transport networks or in predicting and explaining anomalies in traffic conditions. Dublinked and other initiatives (Data.gov,2013, Data.Gov.UK, 2013, Data.Gouv.FR, 2013, Spaghetti, 2013, Dublinked, 2013) have been created to harness this rich resource of information.
Collection, aggregation, organisation, tracking and synchronisation of different data types, i.e., static and dynamic data (through streams) and from various sources, i.e., social and sensor data, are all key challenges that need to be highly considered. Accessing and managing heterogeneous data, together with maintaining a coherent, robust, optimal and up-to-date view is a complex task mainly due to the large amount of data that needs to be exploited (Auer et al., 2007), (Bizer, Heath & Berners-Lee, 2009).
Recent works and funded projects (e.g., EU FP7 PETRA) make use of Big Data for enabling much more advanced systems to deal with transportation means. A large portion of this work focuses on using data from different transportation models, e.g., buses, bikes, taxi, trains for multi-modal journey planning. Many mobile apps have been developed and customized in large cities of Europe. Dublin is one example of such customization. While voice-based interaction is not part of the focus of these apps, this trend shows the benefits of exposing data as open data, and make use of such rich, while heterogeneous information.