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MATSim demand is given by plans.  Each plan is a sequence of activities (home, work, leisure, ...), together with locations and (typically) end times. Activities at different locations are connected by legs.  Trips are constructed from multiple legs; for example "home - walk - bus - train - walk - work" is seen as a multi-modal "walk-bus-train-walk" trip.

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Each agent has several plans.  One of them is "selected" (see "choice" below).  During the network loading (called "mobsim" or "synthetic reality" in MATSim), each agent synthetic traveller synthetically executes her plan.  This essentially means: starting at the first activity given in the plan, departing at its pre-determined activity end time, following the travel description in the plan including possibly being stuck in congestion or not being able to enter a bus, eventually arriving at the second activity and staying there until its end-time, etc.  For within-day re-planning see below.

The MATSim mobsim is a model that treats all entities (persons, vehicles, links, intersections, signals, ...) individually (i.e. a "microscopic" simulation), but apart from that often at a not very high level of detail (which would make the vehicular dynamics a "mesoscopic" model).  Within these constraints, it attempts to be fast – typically simulating 24h of with several millions of travellers in about a minute (using a 1% sample) or in about 10 minutes.

The vehicular dynamics is a queue model; recent versions include full kinematic waves (with piecewise linear fundamental diagrams) obtained from a double-ended queue.

Public transit is fully schedule-oriented, i.e. there are pt vehicles have (possibly automatic) drivers which follow their pt plans, loading and unloading passengers as they go.

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• Scoring.  After plans were executed in the network loading (called "mobsim" or "synthetic reality" in MATSim), there they are given a score.  The score is similar to what we know from random utility models, but is valid for the full simulation period (typically a day).  It gives positive score contributions to activities, and (typically) negative score contributions to travel.
  Scoring is fully configurable (can be programmed in Java).
• Choice.  Choice is essentially just a plain multinomial logit model, between all plans in an agent's choice set, based on the score.

Non-iterative (= within-day re-planning) capabilities

MATSim

MATSim primary design goal was fast iterations while keeping the composing elements microscopic, and within-day re-planning, albeit clearly important, was not fully consistent with that design since it makes the network loading slower.  In the meantime, it has evolved to something that is actively used, for example for the simulation of autonomous vehicles.  The approach is that the mobsim "asks" the person certain things at certain points in time, e.g.:

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