The patterns of behavior of mobility are fluctuating due to socioeconomic and technological reasons. So much so that the big car companies are investing in smart mobility solutions, thus transforming their traditional business model to one closer to the vehicle-as-a-service model.
Not only are they changing their patterns of behavior towards private vehicles, but there is a growing surge of new business models that point to other means of transportation (electric bikes, bicycles, electric skates…) focused on a more sustainable driving network.
Although these business models are appearing in the main urban hubs, in no time they will make their way into medium-sized cities as well. The adoption and the specific needs of the urban layout of the cities, their geographical disposition, population density, and growth make an analysis necessary of both the behavior patterns of users as well as one for the specific needs of their own distribution.
At ACCIONA, we are seeking analysis tools that are able to manage large data sets and establish supply and demand in real-time. How can we develop a specific model of mobility for each city depending on the adopted solution? How can we capture, manage, and exploit that data? How can we manage demand peaks adjusting supply? How can we detect the areas where mobility shared mobility