City authorities, businesses, universities, transport operators, hospitals ... all have data that can be consolidated to build data-driven services and provide a better qualifity of living for citizens.
And with the development of the Internet of Things cities are experiencing a relentlessly growth of data supply. So there is little doubt that modern cities currently generate torrents of data. But Big Data does not mean Smart Data.
To enhance the value of their data, cities and their data hub partners should focus on the preparation of actionnable data that can be used by the developers of new applications and services. Preparing and enriching data for well identified use cases provides a much higher level of agility to the smart city ecosystem and more opportunities to leverage the power of artificial intelligence.
Helping citizens better organize their mobility is a top priority for smart cities. Artificial Intelligence can be used to design mobility-related services but the first challenge is to acquire the capacity to take into consideration the entire user experience.
As a matter of fact, transportation is more and more multimodal and citizens can select different transportation systems according to their activity, time of the day, local traffic, localization, wheather conditions, events ... just to name a few parameters that can influence predictive models.
Consolidating all the data sets that have an influence on mobility and extracting the actionable data that can feed machine learning algorithms is a challenge that can be handle by a data preprocessing platform taylored to the unique requirements of mobility management.