All explainers · Agent-based simulation, explained
The scoring function at work
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In one sentence
Travel is judged by what it does to the day — that is what the person compares.
Why it matters
One person, one day, three plans. The score accumulates through the day: activities add, trips subtract. Walking the shop tour instead of taking the bus saves waiting, in-vehicle time and two transfers — plan 2 scores higher and is kept. Walking the leisure tour as well cuts leisure to 30 minutes and pushes the evening later — the lost activity utility outweighs the saved penalties, plan 3 is dropped.
Agent-based simulation is usually explained with equations or with a screenshot of a running model. Neither shows why a planner should care. These explainers show the mechanism itself — one person, one day, one score — so that the results on a dashboard stop being a black box.
Sources
- Scoring parameters are typical MATSim values (illustrative); see Horni, Nagel & Axhausen (2016), ch. 3
- Charypar, D. & Nagel, K. (2005): Generating complete all-day activity plans with genetic algorithms. Transportation 32(4)
What you see
- 00.0Parameters (Table 11)
- 02.0Plan 1: all tours by bus
- 08.0Plan 2: shop tour on foot
- 14.0Plan 3: leisure tour on foot too
- 20.0Plan memory: 2 kept, 3 dropped
- 22.5Takeaway
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The same person’s day, played out
Every activity has a place, a start and an end; every trip a mode and a route — that is what the simulation moves through the network.

Charypar–Nagel: how an agent values time at an activity
A plan scores well when time is spent at activities close to their typical duration — travel is what takes that time away.

The MATSim loop
The converged state is what scenarios are compared in; calibration tunes the parameters so that it matches observation.
