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eclipse-sumo/sumo

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4,070 stars·1,738 forks·Python·EPL-2.0·18 viewseclipse.dev/sumo↗

Sumo

SUMO is a microscopic traffic simulator and road network generator designed to model the detailed movement of individual vehicles, pedestrians, and public transport. It serves as a multimodal transport simulator and an autonomous vehicle simulator, enabling the analysis of interactions between different transport modes and the testing of automated driving behaviors.

The project distinguishes itself through its ability to couple simulations with external controllers and network simulators to model vehicle communication and autonomous control transitions. It supports the creation of simulation-ready road networks using open map formats and industry standards, including the integration of geographic coordinates, elevation, and satellite imagery.

The platform covers a wide range of traffic engineering capabilities, including traffic flow calibration, demand modeling via origin-destination matrices, and the analysis of vehicle emissions and power consumption. It provides tools for infrastructure management, such as configuring traffic light schedules and speed limits, and supports the simulation of heterogeneous traffic and road incidents.

The system allows for real-time interaction and manipulation of simulation parameters through a programmatic application programming interface.

Features

  • Autonomous Vehicle Simulators - Provides a platform for testing automated driving behaviors and vehicle-to-vehicle communication by coupling simulations with external controllers.
  • Transport Simulations - Provides a comprehensive microscopic transport simulation for modeling individual vehicle and pedestrian movements.
  • Urban Traffic Simulation - Models the explicit movement of every individual vehicle and pedestrian to analyze detailed urban traffic flow.
  • Road Network Importers - Converts road network data from common industry formats into simulation-ready layouts for vehicle routing.

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  • Socket-Based Control Planes - Exposes simulation state and control via sockets to allow external applications to manage vehicles in real-time.
  • Microscopic Traffic Simulation - Models the detailed movement of individual vehicles, pedestrians, and public transport in large networks.
  • Road Network Generators - Converts map data and standardized road network definitions into simulation-ready environments with geo-coordinates and elevation.
  • Multi-Modal Network Models - Combines different transportation modes like trains and pedestrians within a single environment using a shared network graph.
  • Multimodal Transport Simulation - Simulates interactions between different transport modes including cars, trains, bicycles, and pedestrians within a shared environment.
  • Traffic Control Configuration - Configures traffic lights, variable speed signs, parking areas, and rerouting signage to regulate vehicle flow.
  • Traffic Infrastructure Control - Manages speed limits, vehicle behavior, and traffic light schedules using detectors and automated generators.
  • Traffic Infrastructure Management - Enables the design and control of traffic light schedules, speed limits, and signage to regulate vehicle flow.
  • Travel Demand Generators - Creates realistic vehicle flow profiles using traffic counts, origin-destination matrices, or virtual population models.
  • Programmatic Simulation Control - Provides a programmatic API for real-time interaction and manipulation of simulation parameters and vehicle behaviors.
  • Simulation State APIs - Provides APIs to connect the simulation state to external applications for real-time vehicle management and data retrieval.
  • Behavioral Modelling - Simulates autonomous vehicles by configuring specialized car-following models and adjusting human-behavior parameters.
  • Driving Scenario Simulators - Equips vehicles with automated driving capabilities and control transition devices to test autonomous behavior in virtual scenarios.
  • Traffic Maneuver Simulations - Enables vehicles to simulate driving through oncoming lanes using directional network information.
  • Simulation Parameter Manipulators - Allows real-time modification of simulation parameters and vehicle behavior through a programmatic interface.
  • Heterogeneous Traffic Modelling - Models lane-free or heterogeneous traffic by adjusting the lateral resolution of vehicle positioning.
  • Traffic Incident Modelling - Models accidents and road blockages by simulating halted vehicles and closed lanes.
  • Communication Link Simulations - Models communication link performance by coupling the traffic simulation with external network simulators.
  • Behavioral Model Plugins - Allows the replacement of vehicle movement logic through custom classes that define participant interactions.
  • Combinatorial Route Optimizers - Computes shortest paths and dynamic user assignments to determine optimal vehicle navigation across the network.
  • Custom Vehicle Behavior - Allows creating custom vehicle classes and car-following or lane-changing models to define movement and interaction.
  • GPS Map Matching - Maps GPS coordinates to the road network using map-matching techniques to reproduce real-world vehicle trajectories.
  • Ensemble Simulation Engines - Executes multiple simulation instances with different random seeds to eliminate bias and analyze a range of outcomes.
  • Origin-Destination Matrix Models - Generates vehicle demand and trip chains by mapping traffic flows between specific network nodes using counting data.
  • Real-time Simulation Interaction - Modifies the behavior of simulation objects in real-time through a programmatic control interface.
  • Synthetic Trip Generation - Creates synthetic vehicle trips and routes using scripts to simulate stochastic demand.
  • Traffic Demand Calibration - Ensures steady-state vehicle volumes using insertion delays, circular routes, and rerouters.
  • Traffic Flow Analysis - Calibrates traffic demand, analyzes vehicle emissions, and calculates optimal routes using real-world data.
  • Traffic Flow Calibration Tools - Adjusts vehicle flow, speed, and type distributions based on real-world counting data to improve model accuracy.
  • Vehicle Emissions Analysis - Provides capabilities to track emissions and power consumption for internal combustion, electric, and hybrid vehicle fleets.
  • Emissions Tracking - Tracks emissions and power consumption across diverse vehicle fleets, including internal combustion and electric types.

    • Star history

    Star history chart for eclipse-sumo/sumoStar history chart for eclipse-sumo/sumo

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does eclipse-sumo/sumo do?

    SUMO is a microscopic traffic simulator and road network generator designed to model the detailed movement of individual vehicles, pedestrians, and public transport. It serves as a multimodal transport simulator and an autonomous vehicle simulator, enabling the analysis of interactions between different transport modes and the testing of automated driving behaviors.

    What are the main features of eclipse-sumo/sumo?

    The main features of eclipse-sumo/sumo are: Autonomous Vehicle Simulators, Transport Simulations, Urban Traffic Simulation, Road Network Importers, Socket-Based Control Planes, Microscopic Traffic Simulation, Road Network Generators, Multi-Modal Network Models.

    Which projects share features with eclipse-sumo/sumo?

    Projects with overlapping indexed features include: eclipse/sumo — SUMO is a microscopic traffic simulation suite designed to model the movement of individual vehicles and pedestrians… a-b-street/abstreet — A/B Street is an open-source traffic simulation and urban planning tool that models how cars, bikes, and pedestrians… carla-simulator/carla — CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving… commaai/research — This research framework provides a deep learning driving simulator and a multimodal data pipeline for autonomous… nvidia/isaac-gr00t. udacity/self-driving-car-sim — This project is an autonomous vehicle simulator designed to validate self-driving logic and train deep learning…

    Projects sharing features with Sumo

    These projects share indexed features with Sumo. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
    • eclipse/sumoeclipse avatar

      eclipse/sumo

      4,054View on GitHub↗

      SUMO is a microscopic traffic simulation suite designed to model the movement of individual vehicles and pedestrians across large-scale urban road networks. It functions as a multimodal transport simulator that integrates cars, pedestrians, bicycles, railways, and waterways within a single environment, supported by tools for road network generation and traffic demand modeling. The project distinguishes itself through specialized toolsets for environmental impact analysis, which calculate vehicle emissions and energy consumption for electric and hybrid fleets. It provides comprehensive capabil

      Python
      View on GitHub↗4,054
    • a-b-street/abstreeta-b-street avatar

      a-b-street/abstreet

      8,138View on GitHub↗

      A/B Street is an open-source traffic simulation and urban planning tool that models how cars, bikes, and pedestrians move through real-world street networks. It imports data from OpenStreetMap to build detailed, lane-level road models, then runs discrete-event simulations to analyze travel times, delays, and congestion patterns across different infrastructure scenarios. The project provides an interactive map editor for modifying road geometry, lane configurations, traffic signals, and access restrictions, with full undo/redo support. Users can design low-traffic neighborhoods by placing moda

      Rustgameopenstreetmapseattle
      View on GitHub↗8,138
    • carla-simulator/carlacarla-simulator avatar

      carla-simulator/carla

      14,072View on GitHub↗

      CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving software. It functions as an urban traffic simulator that generates realistic vehicle and pedestrian behavior and as a synthetic sensor data generator producing LiDAR, Radar, and camera data. The platform distinguishes itself through its deep integration with robotics frameworks, specifically providing native connectivity to ROS2 nodes for robotic control and data processing. It supports the training of driving models via imitation and reinforcement learning within a controlle

      C++
      View on GitHub↗14,072
    • commaai/researchcommaai avatar

      commaai/research

      4,121View on GitHub↗

      This research framework provides a deep learning driving simulator and a multimodal data pipeline for autonomous vehicle research. It centers on the creation of synchronized autonomous vehicle datasets, which combine high-frequency vehicle telemetry with camera frames to train neural networks. The project implements a convolutional neural network trainer specifically designed to predict steering angles and vehicle transition states from visual data. It features generative capabilities, using autoencoders and transition models to synthesize driving environments and simulate future vehicle move

      Python
      View on GitHub↗4,121
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