With Urbiverse – SWITCH’s AI simulation platform – we virtually designed and optimized a hypothetical EV ride-hailing service based on real-world GPS data. The tool helped determine the precise resources needed to launch a viable service, eliminating the financial risks of physical deployment. The simulation identified the exact fleet size required for 100% trip fulfillment and proved that strategic placement of just 5 charging zones is more effective than widespread infrastructure.
This case study is for urban planners, mobility operators, and investors who need to make strategic decisions about electric vehicle fleets before committing capital.
In a few pages, you will see how AI simulations can de-risk launches, optimize infrastructure investment, and ensure high service levels from day one.
What you will learn inside:
- How to Find the Optimal Fleet Size: Discover how the simulation pinpointed the exact “sweet spot” number of vehicles needed to guarantee 100% trip fulfillment within a strict 10-minute response window, minimizing idle assets.
- The “5-Zone Rule” for Infrastructure Efficiency: Learn why more chargers aren’t always better. The analysis proved that 5 strategically placed charging zones are sufficient to minimize operational downtime, preventing over-investment.
- How to Minimize “Dead Mileage”: See how proximity-based dispatch logic and spatial optimization reduced the time vehicles spend driving empty to pick up passengers or relocate for charging.
- A Blueprint for De-Risking Launches: Understand how moving from raw GPS traces to actionable operational insights provides a de-risked, data-driven blueprint for urban mobility stakeholders.
- How to Plan for Future Scenarios: Explore how the modular platform allows for testing “what-if” scenarios, such as changes in battery technology or demand patterns, supporting long-term strategic planning.
