Cognitive Computing Can Help to Meet Citizens’ Expectations from Transportation Services

Cognitive computing represents a new era in computing that will have a profound effect on transportation planning and transportation services provided to citizens. Cognitive systems can hCognitive image with no wordselp us amplify our own cognition when analyzing transportation data. These systems will help improve our understanding of transportation problems and enable us to make better, more informed planning or operational decisions.  IBM has introduced cognitive technology platform IBM Watson, which uses the natural language processing and machine learning to reveal insights from large amounts of unstructured data. IBM Watson’s capabilities are very relevant to transportation market, which is impacted by following three disruptive forces:

1) Increased demand for transportation services and higher citizen expectations, which is placing a greater pressure on transportation capacity and the quality of transportation services. This requires transportation organizations to operate smarter and more efficiently.

2) Increased transportation ecosystem complexity is introducing challenges in decision making related to the investment and daily operation management.  This is increasing the need for enhanced innovation in investment allocation during the planning, procurement, implementation and operation of  transportation services.

3) Stagnant economic growth and budget constraints has been limiting investment into transportation services for some time. Today transportation organizations are even more challenged to do more with less — requiring them to better leverage existing resources and available data.

The Internet of Things has been also dramatically transforming the transportation market with people, vehicles and the transportation modes (road, air, rail, water) getting connected and generating a vast amount of data. Hence, the many organizations need to address challenges related to the data feeds and data storage. Well managed and organized data repositories will improve results from the traditional modelling tools used today, but it will also open doors for the predictive analysis. Why is this important? The transportation domain has been generating massive amounts of structured data from tolling, connected vehicles, public transport, traffic management and asset management systems. However, whilethis data has enormous potential value for improving transportation services, it is not fully being leveraged and exploited across of different modes of transport.

Further, by applying cognitive technologies and broadening the scope of analysis to all forms of data from the transportation ecosystem, we could unify the view of information from multiple data sources to enable better decisions by enhancing predictive analysis with the cognitive insights from unstructured data. It will help to uncover trends, patterns and relationships which are not currently possible to discover with traditional analytics tools. Imagine the possibilities for improving both short and long-term transportation planning by analyzing  information from the multiple sources and types of unstructured data (e.g., research papers, news articles, legislation, tax data, procurement contracts,  opening hours of business, schools and social network data) which could also serve to bring the citizens’ sentiment into decision making process.

Finally, the potential to pull in insights from structured and unstructured data into real-time applications can enable transportation service providers to deliver new advanced services for citizens has the potential to be a game changer.

Link to the latest IBV studies and IBM Watson to learn more about future of cognitive computing and get familiar with IBM cognitive computing platform.

Please join in the discussion. Leave me a comment, hook up with me on twitter (@miroholecy) or LinkedIn (

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