Big Data and Open Data help develop intermodality and adapt this one in real time, while creating new uses in terms of moves. Many cities offer public transport users several possibilities and favor intermodality. So in San Francisco navigation applications such as Google Maps calculate various multimodal routes by integrating transport and taxi on demand (Uber).
Grenoble transport service, meanwhile, takes into account more options. Users have at their disposal an application indicating which is the most relevant travel mode: tram, bus, walk or car sharing, depending on traffic.
Cities with mobility data can offer alternatives to their resident and understand what travel are the masterplans in terms of moves in order to optimize them.
Dublin, which does not have a metro, had to reduce its bus fleet. However this loss has been accompanied by a new network management tool based on Big Data. The management system created by IBM reduces their travel time by 10 to 15% by identifying in every route which recurring place are problematic in order to make the necessary adjustments. As part of the HubCab project MIT researchers analyzed 150 million courses. They concluded that the number of trips could be reduced by 40% if they were grouped because of all the people going to the same places at the same times.