DATA SIM: Data Science for Simulating the Era of Electric Vehicles (R-3320)

DATASIM aims at providing an entirely new and highly detailed spatio-temporal microsimulation methodology which is grounded on massive amounts of big data of various types and from various sources, with the goal to address the detailed nation-wide consequences of a massive switch to EV, given the intertwined nature of mobility and power distribution networks. While the increasing availablility of big data about human activities provides radical new ways of understanding the social and ecological universe, it is our ambition in this project to complement this information with behaviourally rich data to increase overall knowledge. In terms of interdependencies, the advanced integrated methodological environment that we propose, allows for more realistic and consistent linkages across travel choices made by the individuals in the course of a day than conventional models, with the goal of simulating millions of individual agents, each with their detailed prediction of every activity-travel schedule, enabling more detailed segmentations based on user profile of the agent, different activity types, trip duration and driving ranges. Significant breakthroughs can be gained from the project which lead to novel dimensions of use, each of them situated along the milestones that were set forward in the 'European Industry Roadmap for the Electrification of Road Transport from today till 2020'. Finally, many scientists have already pointed out that the goal of social sciences is not simply to understand how people behave in large groups, but to understand what motivates individuals to behave the way they do. This fundamental insight which can be gained from this project is a step forward towards the solution of this important challenge we face in our society, and it can help us in the longer run to have an impact on overall and wider societal well-being.

Keywords:Data mining, electrical vehicles, environment, network science, transportation

Disciplines:Civil and building engineering, Mechanical and manufacturing engineering, Economics and business