"The willing to move"

Predictive demand is crucial when planning the city. Knowing how drivers, pedestrians, or traffic itself will behave after the implementation of certain measures constitutes a supporting tool for city decision makers. In this sense, Movēre’s simulation algorithms and analysis are adapted for each case and performed under MATSim (ETH Zurich developed) open-source framework, as a demonstrated reliable tool for implementing large-scale agent-based transport simulations.

  • How traffic may be affected if we replace a car-dedicated lane by a bike one? Is it really worthful?
  • Will that new projected tramway beneficiate more commuters if shaped alternatively?
  • Is a Congestion Charge Zone a good measure for a given city? How large should it be to be effective?

  • Even more challenging, how electricity demand will response if new charging facilities are located outside the city?
  • Is cost-free public transport actually promoting sustainable mobility?
  • Or how changing prices in free-floating e-scooters may change commuters behaviour?

  • Multi Agent Modelling

    Framework for demand-modelling are some of the solutions in movēre to simulate human behaviour and support planning decisions. As agent-based modelling, the simulations have the ability to predict decisions per unit (commuter) and its interaction with other agents in the model.

    Analysis are powered by MATSim, a ETHZ developed open-source software to implement large-scale agent-based transport (Public and Private) simulations

    Experiences

  • Greater Geneva and Nice Region Multimodal (Public and Private transport) Macrosimulation
  • 10+ district-scale microsimulations
  • Co-creation of a non-concurrent Land-Use and Transport Facility Location Simulator - FaLC project
  • GIS (Geographic Information System) integration during data collection, analysis and representation

    Furthermore, modelling goes beyond the traffic itself and support further analysis such as road collisions analysis or noise modelling

  • All in all, modelling support decisions by evaluating existing conditions and projecting future effects and needs