Basic Info
Roughly extracted information from MATSim's user guide, MATSim's tutorial slides and the MATSim Book.
References
Official MATSim Resources Overview of considered SW solutions
Introduction
Microscopic modeling of traffic: MATSim performs integral microscopic simulation of resulting traffic flows and the congestion they produce.
Microscopic behavioral modeling of demand/agent-based modeling: MATSim uses a microscopic description of demand by tracing the daily schedule and the synthetic travelers’ decisions. In retrospect, this can be called “agent-based”.
Computational physics: MATSim performs fast microscopic simulations with 10^7 or more “particles”.
Complex adaptive systems/co-evolutionary algorithms: MATSim optimizes the experienced utilities of the whole schedule through the co-evolutionary search for the resulting equilibrium or steady state.
Terminology
Please, refer to MATSim docs
Agent-Based Transport Simulation
Agent: A synthetic person, part of a synthetic population.
Plan: the intention of an agent, typically for one day."going to work at 07:30, go shopping on the way home at 17:00, be home at 17:45".Each agent needs at least one plan.
Choice set: “plan set” of an agent
Choice set generation: time mutation/re-route/...; innovation of existing plans
Choice: replanning - random replanning strategy
Convergence: learning rate
Score: utility of a plan after it was simulated.
Scenario, Model: several datasets and parameters describing infrastructure (supply) and demand in a region. "Scenario" and "model" are often used without differentiation in MATSim's context.
MATSim Loop
Scenario data: description of infrastructure (road network, transit schedule, ...) and agents.
Execution: Agents' travel plans are executed in parallel.A.k.a.: mobsim (mobility simulation), synthetic reality, network loading, traffic flow simulation
Scoring: each executed plan obtains a score.
Replanning: some agents change plans or create new ones.
Analyses: traffic volumes, average speeds, utility changes, emissions, accessibility, …
Iterations: execution, scoring and replanning are iteratively performed.
Optimization: each agent tries to optimize its day.
Evolutionary algorithm: each agent has a set of plans ("choice set"), adding new plans(replanning), removing bad ones after a while.
Co-Evolutionary algorithm: If a plan performs good or bad also depends on the plans of all the other agents.
Nash-Equilibrium: Each agent tries to optimize its plan egotistically. (≠ system optimum)
Other
Plans (input) vs. events (output)
Legs vs. Trips
Plan contains “activity” (e. g. working) and “legs” (going to work by car)
Trips - moving from one node to another with a 0 duration activity
Simulation Stages
Initial demand - describes mobility behaviour (list of agents and their plans)
Execution - “mobsim”, agents and vehicles are moved around in the network
Scoring - after execution of the plans end, the plans are evaluated based on the execution
Replanning - performed by “strategy modules”
Analysis - at the end of complete simulation, performed automatically or separate post-process
Scoring - Utility and Fitness
Compares different plans.
Negative utility (agents travelling; monetary costs - tolls, fares; arriving late; leaving early)
Positive utility (agent performing activities)
Higher score means a “better” plan (better performance).
Each agent maintains multiple plans for the day, which are scored when the plan is executed (in the mobsim), selected and sometimes modified.
Phases
Can be configured in “planCalcScore” module.
Mobsim - the mobility simulation takes one “selected” plan per agent and executes it in a synthetic reality.
Scoring - the actual performance of the plan in the synthetic reality is taken to calculate each executed plan’s score.
Replanning
PLAN REMOVAL If an agent has more than the maximum number of plans (configurable) then some plans can be discarded (by a plan selector).
INNOVATION For some agents, a plan is copied, modified, and selected for the next iteration.
CHOICE All other agents choose between their plans.
MATSim Inputs
A short description of MATSim inputs for running a plausible simulation.
Network.xml
Roads (car, public transport, subway)
Network change events in a file (for timeVariantNetwork)
Population.xml or Plans.xml (demand)
Agents and their plans
Planning and replanning (a lot of replanning causes fluctuations)
1 or more plans (choice set, gene pool) - 1 is executed and scored
Choose 1 plan, create new one (mutation)
Removing bad plans (survival of the fittest)
Executing a plan (= network loading)
Config.xml
One (and only argument) when running MATSim
“Interface” for simple MATSim usage
What needs to be configured
input files locations("scenario data", "data containers")
computational/performance settings(e.g. number of threads)
scoring
parameters
replanning strategies
replanning strategies' behaviour
analyses/output
Public transport data
Transit schedule of the public transport and how they interact with the network
Deterministic Public Transit Simulation
Cars and buses are simulated in queue network (qsim)
PT should be simulated differently (trains in the queue network run fast because there's no stopping them! - they arrive too early according to the schedule)
The SwissRailRaptor is now available directly in MATSim.
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