Traffic Simulation with SUMO – Simulation of Urban Mobility

By Daniel Krajzewicz. 2010

369 citations recorded by the source corpus.

SUMO is a microscopic road traffic simulation made available as open source under the GPL license. The complete suite includes tools for importing road networks, generating routes from different sources, and two versions of the traffic simulation itself, one started from the command line and one including a graphical user interface. The simulation uses the microscopic, space-continuous and time-discrete car-following model developed by S. Kraus and a lane-changing model developed within the work on the simulation. Traffic assignment is normally performed using the iterative approach formulated by C. Gawron, but further methods, such as a one-shot assignment method, exists. The traffic simulation offers a socket-based interface to external applications, allowing to interact with a running simulation online. Values and states of objects the simulation consists of can be both retrieved and changed. SUMO has been used within different projects both by the DLR and by external organizations. The software and documentation can be accessed at http://sumo.sf.net.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/b543ff6b74c2833b7c447843be3354ac24f125db

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Citations in the corpus

SUMO - Simulation of Urban MObility An Overview (2011, methodology): “Besides disaggregating the matrix, the application also assigns an edge from the road network as depart/arrival position.”
Urban Digital Twins for Smart Cities and Citizens: The Case Study of Herrenberg, Germany (2020, methodology): “This simulation uses a microscopic, space continuous, and time discrete car-following and lane-changing model [23].”
Simulation of Urban Mobility (2013, methodology): “The intended approach in the scope of this project is to model current representative traffic scenarios that take into account the existing infrastructure, a time-varying traffic demand model (differentiating between representative peak and nonbusy periods), and light-signal schedules [5].”