Urban transportation systems face the challenge of managing a complex environment, which includes shared e-scooters, bikes, and cars, alongside evolving commuter patterns and new regulatory demands. Addressing this complexity often results in disjointed data and costly decision-making for operators and city planners. SWITCH, an Italian startup, steps into this landscape by offering agentic AI solutions that focus on real-time simulation and forecasting. This allows stakeholders to transition from reactive to efficient, data-driven operations, ultimately providing better services to urban areas.
In recent developments, the mobility sector has seen several efforts to address similar issues using technology. However, many initiatives have either targeted narrow facets of urban transport or failed to integrate multiple data streams effectively. SWITCH, founded in 2020, differentiates itself by integrating a comprehensive suite of AI tools that serve both operators and local governments. Their system connects planning with operations in real time, enhancing the ability to adjust strategies dynamically based on predictive analytics.
What Led SWITCH from Car-Sharing to AI Mobility?
SWITCH began as a car-sharing application in Rome, aimed at matching vehicle demand and supply. However, the onset of the COVID-19 pandemic prompted a strategic shift. As public transport usage declined, the focus shifted to supporting micromobility and AI-driven solutions. The addition of specialist roles, including a Chief AI Officer, facilitated this transition. As emphasized by Simone Ridolfi, the company’s CMO, constant dialogue with operators has been crucial:
“We never stopped talking to operators. That’s how we built what they actually needed,”
allowing SWITCH to refine its offerings based on genuine industry demands.
Why Are SWITCH’s AI Tools Valuable?
Offering solutions such as Urbiverse for modeling, Urban CoPilot for operational optimization, and the SWITCH AI Agent for decision-making, they’re seen as integral to efficient urban mobility management. Ridolfi highlights the significance of predictive modeling in revolutionizing how data is utilized:
“If you know what the next weeks or months will look like, you can plan and operate far more effectively.”
This technological infrastructure allows for precise demand forecasting, making proactive decisions possible for service providers and city planners.
SWITCH offers mobility operators insights into effective vehicle distribution and utilization, leading to an average 25% increase in operational efficiency. By using predictive analytics, operators can strategically plan vehicle purchases, movements, and market entries, mitigating financial risks. Notably, SWITCH’s ability to simulate and predict operational outcomes helped a mobility company decide against entering a potentially unprofitable market, showcasing the value of data-driven insights.
Moreover, the integration of SWITCH’s AI system enables the rapid transformation of incoming data into actionable suggestions, further aiding strategic decision-making. This capability supports operators in optimizing fleet rebalancing, demand concentration, and event planning, ensuring resources are allocated efficiently to maximize returns.
Collaborating with city authorities, SWITCH’s tools support the evaluation and implementation of mobility policies and infrastructure. For instance, strategic advice on hub placements for micromobility services aids in minimizing clutter and increasing service efficiency. Urbiverse’s ability to generate synthetic data when real-time data is lacking ensures informed policy-making and urban planning, strengthening the coordination between operators and city strategies.
As SWITCH expands, its tools continue to play a pivotal role in refining urban mobility. The mix of public and private funding, along with inclusion in programs like the NVIDIA Inception Program, highlights the potential for widespread impact. In the urban mobility sector, feedback loops and proactive strategies are critical, and SWITCH’s approach has set a framework for addressing these needs effectively.
