(Euphoria): Emerging Urban Futures the Opportune Repertoires of Individual Adaptation
Activity-based analysis and modelling has rapidly gained momentum in transportation, urban planning, and geography. It examines which activities are conducted where, when, for how long, with whom, and the transport mode and route involved at a very fine scale of spatial and temporal resolution. All currently operational models are concerned with daily activity-travel patterns and do not consider any dynamics, illustrating the limitations of current approaches.
This project reflects the ambition to achieve breakthroughs in the analysis and modelling of DYNAMIC activity-travel patterns, integrating long-term, mid-term and short-term time horizons: the research agenda in this field of research for the next decade.
Several PhD projects will analyse and model the impact of innovative policies concerned with urban futures (focusing on new urban forms, creative pricing policies, restricted energy, community-based social networks and personalised guidance systems) on behavioural change in activity-travel patterns. Results will be integrated into a new generation multi-agent activity-based system to simulate primary and secondary effects of various types of policies on dynamic activity-travel patterns and therefore on accessibility, mobility, time use, energy consumption, social exclusion, and economic welfare. A large scale panel survey of dynamic activity-travel patterns (supposed to be the first of its kind in the world), and (virtual reality) adaptation experiments will be used for the analyses and estimating and validating the models. The project will lead to (i) a better understanding and an integrative framework and simulation model to assess the primary and secondary effects of various types of policies on sustainable urban environments in terms of a series of indicators (mobility, accessibility, energy use, etc.), derived from dynamics activity-travel patterns and (ii) guidelines how the effectiveness of such policies can be improved.