The concept and software development of MobiSim are guided by three principles: complex but simple modelling, disaggregated modelling and the possibility of testing a range of scenarios.



Scenarios set the pace


  • Variables influencing daily and residential mobility patterns can easily be modified to simulate numerous scenarios of change.
  • Exogenous variables have been chosen to make it possible to create spatial planning scenarios (e.g. modification of the public transport system) or scenarios of context change (e.g. higher fuel prices). A series of exogenous variables (also non-predictable with the model but valuable for testing different scenarios) define the shape and intensity of residential development: number of dwellings constructed per year, building types (detached housing or collective buildings), dwelling sizes and local built density.
  • MobiSim can make allowance for behavioural changes and assess their consequences (e.g. increased household preference for walking and cycling).
  • MobiSim is designed to simulate the effect of planning policies on mobility patterns and behaviours but not to simulate the economic effects of planning policies. Accordingly, economic mechanisms such as price formation and economic concepts (e.g. bid rent, hedonic prices) are not modelled. Even so, economic variables such as household income, dwelling costs and transport costs are taken into account.
  • The model can be calibrated to include stylized facts representing the current context and current behaviours that will subsequently be modified to create scenarios. Stylized facts are not defined by statistical identification of revealed preferences. However, statistical (or survey) data are used to set realistic value ranges for parameters and to provide rough estimates how variables will evolve.
  • Best practices for MobiSim users are based on comparative analysis of alternative scenarios against a baseline scenario of the continuation of current trends. Comparative use of this kind reinforces the non-predictive objective of the model and avoids the pitfall of results being interpreted in terms of absolute values.


Fully-disaggregated modelling


  • MobiSim is a fully-disaggregated simulation platform. It handles people on the scale of individuals, space on the scale of dwellings and buildings, and time on the scale of the minute for one single day in a year.
  • By disaggregating the population, MobiSim can coherently simulate demographic change making allowance for life-cycle events (determining residential mobility) and fully characterizing individual modal choices and household residential choices.
  • By disaggregating space, MobiSim provides an appropriate solution for simulating pedestrian mobility for short trips (from building to building), for analysing people’s residential preferences and satisfaction on the scale of the dwellings in their local environment, and for designing well differentiated scenarios of residential development based on different kinds of planning and design rules.
  • By disaggregating time, MobiSim enables a high-resolution description of individual schedules and public transport timetables. This means urban rhythms can be precisely depicted at an aggregated level.


Complexity is not optional


  • The large number and diversity of variables involved in spatial dynamics explain the difficulty in forecasting future configurations of residential locations (who will live where in what kind of dwelling?) and daily mobility (who will go where using what transport mode?). MobiSim is designed to cope with this complexity.
  • In MobiSim, complexity arises from having a huge number of variables all interacting. But the rules for generating residential and mobility dynamics remain simple.
  • These rules correspond to disaggregated stylized facts. The less that is known about a phenomenon, the more it is broken down into simple stylized facts.
  • All these simple components are re-aggregated (using aggregation rules) and interconnected (using interaction rules). Aggregation and interaction rules may be fuzzy or uncertain.
  • No equilibrium assumptions are made, and individuals and households do not attempt to achieve an optimum (e.g. they do not maximize their utility under budget constraint).