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    • MARS DSL Language
    • MARS Runtime System
      • Getting started with MARS
      • Basic Concepts
        • Multi-Agent-Simulation
        • Agents
        • Layers
        • Entities
        • Environments
          • SpatialHashEnvironment
          • GeoHashEnvironment
          • SpatialGraphEnvironment
          • CollisionEnvironment
        • Model Setup
      • Model Configuration
        • Agent and Entity Configuration
        • Layer Configuration
        • Output Filter
        • Simulation Configuration Options
        • Simulation Output Formats
      • Data Sources
        • Geospatial Data Sources
        • Geospatial Data Types
        • ASCII grid (.asc)
        • CSV
        • Time-series Data
      • Analysis and Visualization
        • Visualizing Agent Movement Trajectories
        • Simple Live Visualization
        • Analyzing Output Data
      • Tutorials
        • Creating a new MARS C# project
        • Creating a starting point for your model
        • Creating vector and raster layer files
        • Building your first model (wolf-sheep-grass)
        • Common problems and solutions
        • Acquire geo data for layers
        • Build and start your model in Kubernetes cluster
    • SmartOpenHamburg
      • Quick Start
      • Ready to use scenarios (boxes)
        • Ferry Transfer
        • Green4Bikes
        • Results
        • Result Schemas
      • Layer
        • Multimodal Layer
        • Modal Layer
        • Scheduling Layer
        • Vector Layer
      • Agents
        • Behaviour Model
        • Multimodal
        • Multi-Capable
        • Multi-Modality
        • Citizen
        • Traveler
      • Entities
        • Referencing
        • Street Vehicles
        • Bicycle Vehicles
        • Ferry

    Image SmartOpenHamburg

    SmartOpenHamburg (SOH) is a microscopic, multi-modal mobility simulation for large-scale scenarios. The model can be used to build digital twins for decisions support systems.

    Microscopic

    The model implements mobility behaviour utilized by individual agents or by usinng life cycle of citizens. Agents travel to different defined destinations along streets, sidewalks, bicycle paths and ferry connections affecting each other.

    Goals

    The goals of SOH are the conception and realization of a decision support system for large-scale mobility scenarios.

    Goals

    Example

    The following video shows an example scenario using Citizen agents and the daily schedule model for the urban area in Altona Hamburg:

    Lifecycle

    On the way, people can use different modalities to achieve their goals or use publicly available resources. The life cycle of each agent consists of a sequence of consumed services and dayplanning targets given by points of interests (POI) marked in the city.

    The figure below shows the synthetic work target selection based on the provided public land used services.

    POI Hamburg

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