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1. Big data challenges

Big data coming from mobile call records, GPS tracks and social networking sites e.g. Facebook, along with microscopic energy consumption, land-use, road network, and public transport level-of-service data pose an enormous challenge in terms of data storage, integration, management and privacy.

2. Big data joined with behavioral motivation leading to truly novel social science laws

Big data needs to be merged with behaviorally rich activity-travel diaries, generating a novel data-driven theory which enables us to analyze mobility demand from the individual point-of-view, not neglecting the behavioral and contextual situation of the individual.

3. The behavioral sensitivity of the individual as the core entity in the novel simulation standard

Agent-based reality mining of Big data is combined with behavioural sensitivity of the agent, accounting for changes in human behavior when circumstances change, either due to control, e.g. policy actions to prevent peak loads in the power network, or due to general trends, e.g. the use of electric vehicles.

4. A novel standard for evaluation and benchmarking

The massive amounts of Big data can be used to estimate origin-destination matrices, setting a novel, better and more detailed standard for evaluating, validating and benchmarking agent-based microsimulation models.

5. An issue of scalability

Computational power needs to be enhanced by orders of magnitude using state-of-the-art advances in high-performance fine-grain parallel computing systems, addressing scalability problems resulting from the behavioral theory extraction from Big data and from the adoption of this theory in a nation-wide simulation environment of electrification of road transport.