Scenario Simulator

Design a new phenomenon, change your transportation network and population data to launch multiple scenarios to forecast the results with GeoTwin’s agent-based simulator.

Learn more about GeoTwin’s Agent-based simulator

GeoTwin’s Human-Centred Urban Analytics Platform employs a SaaS-based decision-making enabler for organizations that want to understand how cities, populations, and emergent phenomena are shaping up today and, in the future. Also, the organizations that need to evaluate different options/scenarios to support decision-making but are frustrated with the time, cost, and accuracy of generic and old-school data analytics or modeling and simulation tools.

Agent-based modeling captures the behavior and interaction of different agents within an environment, recreating complex dynamics. This means it can solve problems where traditional methods are insufficient, such as what-if analysis in transportation, market research, and retail analytics. Our system uses multi-agent simulation and AI to understand better and predict a city’s future mobility patterns by simulating people’s behavior based on their individual preferences.

We can conduct complex what/if analysis using our GeoTwin scenario-simulator to understand the changes in demographics, land use, infrastructure, and behavioral preferences. As a result, GeoTwin can make highly accurate predictions about future activities in a defined area to answer a wide range of complex questions such as:

  • Accessibility and attractiveness of a real estate investment; who will be the potential customers, where they live and work, and why and how they move?
  • Model the spread of disease in a given city, map and predict the best lockdown strategy to reduce the transmission.
  • Analyze the new market opportunity for the new mobility offer, which city to invest in, why, what will be the demand? Who will be the customer? Ideal pricing to catch the customers from the competitors.
  • Estimate the demand par modes and also what type of solutions are required based on the commuter type and trip purpose.
  • Where are the five best locations to place digital billboards on the outskirts of a city to maximize morning business commuter views?
  • What will be the EV vehicle market penetration rate by 2024 or 2030 in a given city, and where to deploy the relevant charging point structure? Potential demand Vs ideal pricing?

What kind of modes does GeoTwin model?

  • Driving (private auto): Trips made by drivers in private auto vehicles. This is equivalent to the number of private auto vehicle movements.
  • Walking: Trips made by people walking.
  • Public Transportation: Metro, bus and tramway lines
  • Biking: Biking only trips.
  • Public Transit: Trips that primarily used public transit. For example, buses, light rail, and subways.
  • On-demand Mobility Services: car sharing, pooling services type Uber, or dynamic bus line…

Why is GeoTwin different from its competitors?

  • We have a whole platform ready-to-use, so conducting a whole simulation does not require any technical skills.
  • We can easily adapt a sub model to your needs, as we are a multimodal-based simulation.
  • We can scale up easily thanks to our cloud-based architecture. We take care of all hardware requirements.
  • We are API based so we can interact with your different already existing tools that you want to incorporate in our simulator such as MATSim or other private simulators.

We are proposing more than 45 different key metrics as an output of our simulator. The key metrics concern the demand assessment, operational efficiency and ecological and economic aspects

Why Simulation ?

Many of today’s problems are too complicated for simple structural models and cannot be handled with just machine learning or statistical techniques.

With our software, you can explore any number of different future scenarios to identify strategies and tactics that work under a wide range of possible outcomes.

Capture emergent phenomena - large - scale behaviour that impossible to predict using traditional methods as they result from the local interactions between agents as well as the transportation supply.