For years, car sharing has been one of the symbols of new urban mobility: fewer private cars, more sharing, greater sustainability. Today, however, the sector is going through a slowdown in Italy as well, with reduced services and operators rethinking their business models.

Reading this scenario as a failure would be an oversimplification. Car sharing is not disappearing; it is changing its role within a mobility system that has become more complex and more mature.

A comparison with other European countries helps to better understand this transition. In cities such as Paris, Berlin or Amsterdam, car sharing continues to play a relevant role because it is embedded in an urban ecosystem strongly oriented toward reducing private car ownership, supported by efficient public transport, disincentives to car possession and planning designed for intermodality. In these contexts, shared cars tend to address specific needs or replace a second private vehicle.

The Italian context, however, is different. Our cities were not designed for shared mobility, private cars remain central to daily habits, and public transport—especially outside major urban areas—does not always offer a fully effective alternative. In this scenario, car sharing struggles to become a structural solution, particularly for those who travel daily over medium-to-long distances or in peri-urban areas.

Yet while traditional car sharing is slowing down, smart mobility is accelerating. The most significant change in recent years does not concern the vehicle itself, but how it is used and understood. Value has gradually shifted from vehicle ownership to the ability to analyse mobility patterns, driving behaviour and usage contexts.

This is where data and artificial intelligence come into play. Today, mobility can be managed through insights that make it possible to understand when, where and how people move, and to design services that are more efficient, sustainable and aligned with real needs. The focus is no longer on increasing the number of available vehicles, but on using resources more intelligently, as already happens in many advanced urban mobility models worldwide.

The future of urban mobility will not be defined by a single winning solution. Instead, it will be an integrated ecosystem in which car sharing, rental, corporate fleets, micromobility and public transport coexist and interact. In this model, car sharing does not disappear but becomes part of a broader Mobility as a Service approach, where the goal is not to provide a car, but to simplify and optimise mobility.

Artificial intelligence plays a key role in this evolution. Through predictive models and advanced analytics, it becomes possible to anticipate demand, optimise fleets, reduce operating costs and improve the user experience. In other words, fewer vehicles, but more efficient ones, embedded in a system capable of adapting to urban contexts and people’s needs.

It is this ability to read and interpret mobility that represents today’s real challenge. A shared challenge, involving both public and private stakeholders, and requiring tools, skills and vision to support the ongoing transformation. Because the future of mobility is not about a single solution, but about an intelligent system in which every actor can find their place and create value.

Predictive maintenance is no longer a competitive advantage reserved for a handful of early adopters: it has become a strategic lever for all companies managing fleets and aiming to ensure operational continuity, cost efficiency, and vehicle safety. Increasingly complex supply chains, rising spare parts prices, pressure on Total Cost of Ownership, and the growing adoption of electric and hybrid vehicles are accelerating an ongoing shift: moving from reactive maintenance to a truly intelligent model capable of anticipating failures before they occur.

This is precisely the goal of predictive maintenance: identifying anomalies and early warning signals that precede a malfunction, enabling intervention before a fault results in vehicle downtime, additional costs, and service disruptions.

For years, maintenance followed linear logic—fixed mileage, calendar schedules, planned replacements, and emergency interventions whenever a vehicle broke down. But this approach is no longer sustainable. The predictive model introduces a paradigm shift: it continuously analyzes vehicle usage data, monitors its evolution over time, and flags when a component begins to behave abnormally. The objective is to detect, as early as possible, the signals that anticipate a failure.

The strength of predictive maintenance lies in the quality of the data feeding the models. The most relevant inputs come from:

• dynamic vehicle parameters: acceleration patterns, vibrations, temperatures, ignition cycles.
• real usage data: driving style, journeys, loads, and operating conditions.
• maintenance history: past interventions, replaced components, mileage.
• environmental variables: seasonality, road conditions, geographic context.

Telematics plays a crucial role, enabling the collection of consistent, accurate data directly from the fleet. This makes it possible to analyze not an “average” vehicle but the real behavior of each individual asset within its specific operating context. Artificial intelligence learns what is “normal” for a given vehicle and its history. When a significant deviation occurs—such as a temperature fluctuation, an unusual vibration pattern, or abnormal consumption—the system detects it and calculates a corresponding risk level.

The aim is not to generate generic alerts but to provide a clear assessment. This allows fleet managers to schedule maintenance activities proactively and more precisely, avoiding unexpected downtime.

The benefits of predictive maintenance become evident after just a few months of use. The first and most tangible impact is a reduction in unplanned downtime—one of the costliest issues for fleet managers. Anticipating a failure means avoiding sudden breakdowns and maintaining service continuity. In addition, predictive planning enhances the overall lifecycle of the vehicle: intervening only when necessary—neither too early nor too late—extends component longevity and helps avoid premature replacements. Maintenance becomes more accurate and less wasteful, with truly targeted interventions.

The result is a more reliable fleet and, consequently, greater driver safety, as vehicles are better monitored and less prone to critical failures. All of this contributes to a significant reduction in TCO, thanks to improved management of spare parts, workshops, and vehicle downtime. Predictive maintenance not only enhances fleet performance but also makes overall operations more sustainable, both economically and operationally. For rental companies, it also ensures better fleet rotation, fewer “off-rent” vehicles due to breakdowns, and greater punctuality in vehicle deliveries.

A Lever for Sustainability

Predictive maintenance is also a powerful ally from an ESG perspective. More targeted interventions mean fewer wasted components and materials, fewer unnecessary trips to workshops, more energy-efficient vehicles, and increased overall safety. The result is a more responsible fleet management model that balances operational efficiency with environmental sustainability.

This approach also demonstrates how telematics can evolve from a simple monitoring tool into a strategic enabler for managing risk and operational efficiency. Its value lies in the ability to correlate different types of data—mechanical, behavioral, environmental—and transform them into actionable insights. For fleets, this means shifting from a management model based on fixed schedules and reactive responses to a truly data-driven process.

More broadly, adopting predictive maintenance signals a genuine shift in mindset. It is not just about adding a new tool but embracing a model that anticipates problems rather than chasing them—turning data and signals into operational decisions that make fleets more efficient, safer, and more sustainable. For fleet managers, this translates into uninterrupted service, better cost control, and a more resilient approach to vehicle lifecycle management. For companies, it means building processes that are stronger and future ready.

When technology allows us to prevent what once could only be managed after the fact, maintenance is no longer an unavoidable expense: it becomes a strategic lever that creates value.

In recent years, motorcycles, scooters and—more recently—e-bikes and kick scooters have become major players in our cities. They are fast, agile vehicles, ideal for moving through traffic and navigating areas where parking is difficult. At the same time, however, those who use them know very well that being more exposed on the road also means taking on greater risks.

Safety on two wheels is a topic that regularly returns to the center of public attention, especially because it takes very little to end up in a dangerous situation: a distraction, sudden braking, or an unexpected oil patch on the asphalt. This is why today, alongside traditional safety measures, modern tools capable of making a real difference are becoming increasingly important. Among these, technologies for data collection and analysis play a leading role.

Riding on two wheels means experiencing the road in a direct and immediate way: there are no protective barriers, balance is more fragile, and the rider’s reaction time matters more than ever. Several factors can impact safety, including limited visibility to other road users, road conditions, weather, sudden changes in traffic flow, and riding habits that are not always fully aware. In a context where light mobility continues to grow rapidly, potential risk situations increase as well. This is where intelligent tools can offer concrete support.

Telematics makes it possible to collect, analyze, and interpret a large amount of data that helps us better understand what happens during a ride. It is not just a monitoring system, but a true ally capable of improving safety in practical and immediate ways. Thanks to sensors, it becomes possible to observe the vehicle’s real behavior—from harsh braking to sudden acceleration, from corners taken at excessive speed to moments in which traction is lost. All this information becomes a kind of “mirror” for the rider or for fleet managers, providing a clear view of driving habits and helping identify where improvements can reduce risks.

The integration of artificial intelligence further enables the identification of recurring patterns and potentially dangerous situations, such as stretches of road where more accidents occur, weather conditions that increase the likelihood of falls, or times of day when certain risky manoeuvres occur more frequently. This predictive capability opens the door to a completely new approach—not just intervening after an incident but preventing it before it happens.

Another equally important aspect concerns emergency management. The most advanced solutions can automatically detect a fall or impact — the so-called crash detection — and send an immediate alert with the precise location and essential information about the event. In many cases, especially outside urban areas or at night, the speed at which help is activated can make a significant difference. Technology also plays a key role in protecting the vehicle itself. Riders know how vulnerable motorcycles and scooters are to theft, and tools such as real-time tracking, unauthorized-movement alerts, or customizable “safe zones” provide valuable support in reducing risks and improving vehicle recovery.

Ultimately, safety is not built on rules and devices alone: it is a balance between conscious behavior and the tools that help protect us. Digital solutions enter this balance discreetly but with significant impact. They make riding more informed, support fleet managers, promote a culture of prevention, and contribute to reducing risks.

In a future where two-wheel mobility will continue to expand, equipping these vehicles with intelligent systems is no longer optional—it is a natural step toward safer, more responsible travel.