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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.

Vehicle type and route distributions

The easiest way of dynamically modifying vehicle behavior is to choose the type or the route from a distribution on loading. Each type / route has to be given explicitly with an assigned probability, see route and vehicle type distributions. This gives on the one hand fine grained control of the results but on the other hand makes it hard to model something like for instance a uniform distribution of vehicle lengths between 5m and 7m.

Speed distribution

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 <vType>-attribute speedFactor which 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.


The default car-following model Krauss supports stochastic driving behavior through the vType-attribute sigma (default 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 this attribute.

Departure times

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 in time).

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 vehsPerHour,number or period), a vehicle will be emitted randomly with the given probability each second. This results in a binomially distributed flow (which approximates a Poisson Distribution for small probabilities). When modeling such a flow on a multi-lane road it is recommended to define a <flow> for each individual lane.

Departure and arrival attributes

The <flow>, <trip> and <vehicle> elements support the value "random" for their attributes departLane, departPos, departSpeed and 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).

Further sources of randomness