Creates a vehicle type distribution by sampling from configurable value
distributions for the desired
python tools/createVehTypeDistribution.py config.txt
The only required parameter is the configuration file in the format shown below (example config.txt):
tau; normal(0.8,0.1) sigma; normal(0.5,0.2) length; normal(4.9,0.2); [3.5,5.5] param; myCustomParameter; normal(5, 2); [0, 12] vClass; passenger carFollowModel; Krauss
In the config file, one line is used per vehicle type attribute. The syntax is: [param; ] <AtrributeOrParameterName>; <ValueOrDistribution> [; <limits>]
If the prefix param is given at the beginning of a line, it is assumed
that the values of a vehicle parameter (given as a
element of the
vehicle element) are to be sampled. Otherwise, values
of an attribute of the
vehicle element are sampled.
ValueOrDistribution can be a string, a scalar value or a distribution
definition. Available distributions and its syntax are:
- "normal(mu,sd)" with mu and sd being floating numbers: Normal distribution with mean mu and standard deviation sd.
- "normalCapped(mu, sd, min, max)" By default, no negative values are accepted but may be enabled by setting a negative lower limit.
- "lognormal(mu,sd)" with mu and sd being floating numbers: Normal distribution with mean mu and standard deviation sd.
- "uniform(a,b)" with limits a and b being floating numbers: Uniform distribution between a and b.
- "gamma(alpha,beta)" with parameters alpha and beta: Gamma distribution.
- --output-file configures the name of the output file to be written
- --name Name of the created distribution
- --size Number of s to be sampled for filling the distribution
- --seed Set the seed for the random number generator
Retrieving parameters from measurements of individual vehicles#
To obtain mean and deviation a number of values must be obtained from the data set. The following is recommenced:
- accel: the maximum (or high percentile) acceleration for each vehicle
- deccel: the maximum (or high percentile) deceleration for each vehicle
- speedFactor: the maximum (or high percentile) quotient of speed/speedLimit for each vehicle
This scripts extracts test scenarios if you like to run a simulation scenario which is included in the test folder
python tools/extractTest.py <path to test directory>
- or use the online test extraction. In the online tool you enter the path to the test you like (e.g. <SUMO_HOME>/tests/sumo/extended/rerouter/use_routing_device into the form and get a zip containing all the files.
This script generates parking lots. Example:
python tools/generateParkingLots.py -b <xmin, ymin, xmax, ymax> -c <connecting edge> [-i <parking-id> -n <number of parking spaces> -l <space-length> -a <space-angle> ...]
python tools/generateParkingLots.py -x <x-pos> -y <y-pos> -c <connecting edge> [-i <parking-id> -n <number of parking spaces> -l <space-length> -a <space-angle> ...]
The required parameter are the shape (--bounding-box) or the position (--x-axis and --y-axis) of the parking lot and the connecting edge (--connecting-edge). More options can be obtained by calling python tools/generateParkingLots.py --help.
- --parking-id defines the name/id of the parking lot
- --parking-spaces defines the number of the parking spaces
- --start-position defines the begin position of the entrance/exit of the parking lot
- --end-position defines the end position of the entrance/exit of the parking lot
- --space-length defines the length of each parking space
- --space-angle defines the angle of the parking spaces
- --x-space-distance defines the lateral distance (x-direction) between the locations of two parking spaces
- --y-space-distance defines the longitudinal distance (y-direction) between the locations of two parking spaces
- --rotation-degree defines the rotation degree of the parking lot
- --adjustrate-x defines the modification rate of x-axis if the rotation exists
- --adjustrate-y defines the modification rate of y-axis if the rotation exists
- --output-suffix output suffix
- --fullname full name of parking area
- --verbose tell me what you are doing
This script generates a pedestrian edge for each public transport stop (in the form of .nod.xml and .edg.xml files. The output is suitable for extending rail-only networks with the bare minimum of pedestrian infrastructure for departing, changing trains and arriving. Example:
python tools/generateStationEdges.py rail.net.xml stops.xml netconvert -s rail.net.xml -e stops.access.edg.xml -n stops.access.nod.xml --ptstop-files stops.xml -o railForPersons.net.xml --ptstop-output stopsWithAccess.xml
This script generates rerouter definitions for a continuously running simulation. Rerouters are placed ahead of each intersection with routes leading up to the next intersection and configurable turning ratios. Vehicles that enter the simulation will circulate continuously (unless hitting a dead-end). Example:
python tools/generateContinuousRerouters.py -n <net-file> -o <output-file>
This script generates parking area rerouters from a parking area definition. Example:
python tools/generateParkingAreaRerouters.py -n <net-file> -a <parkingArea-file> -o <output-file>
This script runs a given sumo configuration multiple times with different random seeds and averages the trip statistics output (see trip statistics).
python tools/averageTripStatistics.py <sumocfg-file>
As default, the simulation will be run 10 times with an initial seed for random seed generation of 42. These values can be changed with the options -n and -s respectively.
This script determines feasible stop-to-stop travel times and creates a public transport schedule (regular interval timetable) for all lines. The stop-to-stop travel times are determined on an empty network. Example:
python tools/ptlines2flows.py -n <net-file> -s <ptstops-file> -l <ptlines-file> -o <output-file>
As output, the public transport lines are written as flows. By default a period of 600 seconds is adopted as regular interval, which can be changed with the -p option.
With the option --use-osm-routes, public transport routes from the given osm ptlines-file will be used, rather than creating new shortest path routes between stops.
This script retrieves background images from ESRI ArcGIS tile servers and other imaging APIs such as Google Maps and MapQuest. The simplest usage is to call it with a SUMO network file only. It will generate a visualization settings file containing the coordinates which can be loaded with sumo-gui or netedit. The most useful options are -t for the (maximum) number of tiles to retrieve and -u to give the URL of the tile server. Examples:
- Retrieving data from the public ArcGIS online instance:
python tools/tileGet.py -n test.net.xml -t 10
- Retrieving satellite data from Google or MapQuest (Requires obtaining an API-key first):
python tools/tileGet.py -n test.net.xml -t 10 --url maps.googleapis.com/maps/api/staticmap --key YOURKEY python tools/tileGet.py -n test.net.xml -t 10 --url open.mapquestapi.com/staticmap/v4/getmap --key YOURKEY
The generated setting file can be loaded in sumo-gui with:
sumo-gui -n test.net.xml -g settings.xml
Synchronizes saved state files from a (remote) simulation and replays them in a local sumo-gui instance to observe the remote simulation (requires rsync).
To observer every step in a simulation with step length 1s, the remote simulation must be started with option --save-state.period 1. In order to conserve disk space, the option --save-state.period.keep 3 is recommended. (i.e. to retain only the last 3 simulation state files at any time).
To replay the state files the following call can be used:
python tools/stateReplay --sumo-config replay.sumocfg --src REMOTE_FOLDER --dst LOCAL_FOLDER
The given .sumocfg file only needs to include the network and any additional infrastructure referenced by the remote simulation. The value of REMOTE_FOLDER can be any folder as understood by rsync (i.e. remotehost:~/myfolder)
Option --save-state.period 1 can slow down a simulation significantly.