Stochasticity is an important aspect of reproducing reality in a simulation scenario. There are multiple ways of adding stochasticity to a simulation. They are described below.
Random number generation (RNG)#
Sumo implements the Mersenne Twister algorithm for generating random numbers. This random number generator (RNG) is initialized with a seed value which defaults to the (arbitrary) value 23423. This setup makes all applications deterministic by default as the sequence of random numbers is fixed for a given seed. The seed may be changed using the option --seed <INT>. When using the option --random the seed will be chosen based on the current system time resulting in truly random behavior.
The simulation uses multiple RNG instances to decouple different simulation aspects
- randomness when loading vehicles (typeDistribution, speed deviation,...)
- probabilistic flows
- vehicle driving dynamics
- vehicle devices
The decoupling is done to ensure that loading vehicles does not affect simulation behavior of earlier vehicles. All RNGs use the same seed.
Vehicles can be added to the simulation with a fixed route (
<vehicle>) or with an origin-destination pair (
A third alternative is to specify a set of routes (
<routeDistribution>) and let the vehicle draw a random route from such a distribution. For details, see route distributions.
Vehicle Type Distributions#
A simple way of of modelling a heterogeneous vehicle fleet works by defining a
<vTypeDistribution> and let each vehicle pick it's type randomly from this distribution. For details, see vehicle type distributions.
By default, vehicles in SUMO adhere to the maximum speed defined for the
lane they are driving on (if the maximumSpeed of their vehicle type
allows it). This behavior can be modified using the
makes vehicles drive with that factor of the current speed limit. The
attribute also allows the specification of the parameters of a normal
distribution with optional cutoffs. The random value is selected once
for each vehicle at the time of its creation. Using a speed deviation is
the recommended way for getting a heterogenous mix of vehicle speeds.
By default, a speed distribution with a standard deviation of 10% is active.
For details, see speed distribution
The default car-following model
supports stochastic driving behavior through the
0.5). When this value is non-zero drivers will randomly vary their speed
based on the RNG described above. Other car-following models also use
The departure times of all vehicles may be varied randomly by using the option --random-depart-offset <TIME>. When this option is used each vehicle receives a random offset to its departure time, equidistributed on [0, <TIME>].
Flows with a fixed number of vehicles#
The duarouter, dfrouter
and jtrrouter applications support the option --randomize-flows.
When this option is used, each vehicle defined by a
<flow>-element will be
given a random departure time which is equidistributed within the time
interval of the flow. (By default vehicles of a flow are spaced equally
Flows with a random number of vehicles#
Both duarouter and sumo
support loading of
<flow> elements with attribute
probability. When this attribute is
used (instead of
period), a vehicle will be emitted randomly with the
given probability each second. This results in a binomially
(which approximates a Poisson
small probabilities). When modeling such a flow on a multi-lane road it
is recommended to define a
<flow> for each individual lane.
The effective flow may be higher at lower step-length because the discretization error is reduced (vehicles usually cannot be inserted in subsequent seconds due to safety constraints and insertion in every other second does not achieve maximum flow).
Departure and arrival attributes#
<vehicle> elements support the value "random" for their attributes
arrivalPos. The value will be chosen randomly on every insertion try (for the
departure attributes) or whenever there is a need to revalidate the
arrival value (i.e. after rerouting). The attribute
departPosLat also supports the value "random".
The lateral offset at departue will only affect simulatoin behavior when using the sublane model though it will be visible without this model too.
When setting the lane change mode attribute
lcSigma to a positive value, Vehicles will exhibit some random lateral drift.
Further sources of randomness#
- The tool randomTrips.py allows generating traffic between random edges. It also supports randomizing arrival rates.
- od2trips randomly selecting depart and arrival edges for each trip when disaggregating the O/D-Matrix
- duarouter adds randomness when performing Demand/Dynamic_User_Assignment
- duarouter can randomly disturb the fastest-paths by setting option --weights.random-factor
- Simulation routing can be randomized to ensure usage of alternative routes.