Questions, answered.

Park.Easy is a patented parking intelligence platform that uses connected-vehicle telematics and lightweight edge devices—combined through a patented sensor-fusion model—to understand how vehicles move, park, and leave your assets in real time. It turns those fused signals into a live operating picture you can actually act on.

Camera and per-space sensor systems watch individual stalls or lanes. Park.Easy’s patented use of telematics, combined with edge devices and facility signals, senses from the vehicle itself instead of relying on hardware in every spot. That patented fusion model lets you see entry, circulation, curb, and on-street behavior without blanketing your facility in devices.

No. The dashboard is only one expression of a patented sensing and data layer underneath. That layer continuously captures connected-vehicle telematics, edge-device, and facility signals, then uses Park.Easy’s patented fusion model to resolve them into a coherent operating picture. Dashboards, alerts, reports, and APIs are ways of delivering that patent-backed intelligence, not the product itself.

Park.Easy is the patented operational foundation: it captures and fuses telematics, edge, and facility signals into a trustworthy view of what is happening. Stratum builds on that same patented data layer for decision intelligence—evaluating options, comparing tradeoffs, modeling scenarios, and recommending actions across policies, pricing, and operations.

Park.Easy is built for complex, high-value environments such as universities, hospitals, airports, downtown districts, mixed-use developments, and workplaces. It is especially useful where curb, on-street, and structured parking all interact and where small changes in circulation or policy have outsized impact.

Common questions include: Where are arrivals backing up? Which areas are routinely over- or under-utilized? How do special events change normal patterns? Which policies or prices move behavior in the way we expect? And where can better use of connected-vehicle and telematics data improve the experience for both drivers and staff.

Park.Easy connects to drivers through an app that they download once to register and explicitly opt in. That registration step is important for privacy, consent, and data-access regulation. After a driver is set up, Park.Easy relies on telematics signals—combined with edge devices and facility data—to understand parking activity; in normal use, drivers do not need to keep opening the app for the system to work.

Telematics turns each opted-in vehicle into a moving source of signal. Park.Easy’s patented use of telematics, plus its fusion model, lets you see not just where vehicles end up but how they arrive, circulate, queue, and turn over. That gives you a far richer view of demand, friction, and opportunity than gate counts, cameras, or stall sensors by themselves.

Drivers enroll by downloading the Park.Easy app once and providing explicit consent, which allows Park.Easy to use specific telematics signals for parking and mobility purposes. Those signals flow into Park.Easy’s patented telematics-driven fusion pipeline alongside edge-device and facility data. Access and use are governed by driver consent, deployment purpose, privacy expectations, and applicable regulations.

Drivers are asked to download the app once so they can give clear permission and understand how their data will be used. After that, the goal is for parking to “just work” without managing another app every time: clearer guidance, fewer surprises, less uncertainty about where to go, and smoother arrival and departure. In day-to-day use, most of the benefit comes from how the environment behaves, not from actively using the app.

Yes. Park.Easy’s patented telematics-driven sensing and fusion layer is designed to be a foundation for future V2I. As those capabilities mature, the same approach to capturing and understanding vehicle signals can support richer two-way interactions—more precise guidance, better curb and street coordination, and policy changes reflected directly in the vehicle experience—without rethinking how you source or combine data.

No. Park.Easy uses a limited number of edge devices placed where they have the most coverage value, then extends that with connected-vehicle signals. The goal is to give you a rich operating picture without installing a device at every stall or every lane.

Deployment is designed to be additive, not disruptive. Edge devices are installed in a way that avoids closing the entire facility whenever possible, and the software layer connects to systems you already use. The intent is that your team experiences a smoother operation, not a long construction project.

Park.Easy can start with a single asset and extend to a portfolio. The same platform model scales across multiple garages, surface lots, and curb zones, with device counts tuned to each asset. Portfolio views allow you to compare locations and understand patterns across your network, not just at one site.

Most deployments involve a combination of parking and mobility leadership, operations managers, facilities, and IT. For some institutions, campus planning, transportation demand management, or finance also participate when questions involve policy or revenue.

Most organizations start seeing useful insights within the first weeks of live operation. As the system observes more patterns over time, it becomes easier to understand outliers, seasonality, and the impact of specific changes.

Park.Easy can integrate with common parking and mobility systems including LPR, PARCS, permit systems, payment providers, and select third-party data sources. The goal is to add vehicle-level context around the systems you already run, not to replace them.

No. Park.Easy is meant to be augmentative. It sits alongside your existing stack to give you a clearer, more connected view of activity, so that the investments you already have—gates, LPR, permits, payments—become more valuable.

You can access data through dashboards, standard reports, exports, and APIs. Many operators rely on out-of-the-box views for daily work and use exports or APIs when they want to feed data into existing BI tools or planning workflows.

Yes. When appropriate, Park.Easy can provide structured outputs that align with your analytics or data warehouse strategy, so you can combine parking and mobility signals with other operational or financial data.

Vehicle and location data from opted-in drivers are used to understand parking activity and operational patterns, not to build marketing profiles. Park.Easy does not monitor or store data that is unrelated to parking sessions or outside the facilities and areas defined in a deployment. Signals are scoped to what is needed to run and improve parking and mobility operations.

No. Park.Easy does not sell driver data to third parties. Data from drivers and facilities is used to support the services organizations sign up for—such as better arrival experiences, operations, and planning—not as an advertising or resale product.

Your organization retains control of its operational data under the terms of the agreement. Reporting, exports, and integrations are structured so you can access the insights you need while maintaining appropriate governance and respecting what drivers consented to.

Role-based access controls allow you to tailor what different users can see and do. For example, an operations lead may see full site activity, while a frontline user may only see the views they need for day-to-day work.

Park.Easy is designed around a simple principle: be helpful when it matters, and respectful always. That means: explicit driver opt-in via the app, use of telematics and facility data only for defined parking and mobility purposes, no selling of driver data, and no monitoring outside the scope of a parking session or outside participating facilities.

You can begin by requesting a technical walkthrough tailored to your environment. That session typically includes your parking, mobility, facilities, and IT stakeholders and walks through architecture, deployment, integrations, and example outcomes.

It helps to have a sense of: the assets you are focused on, the systems currently in place, any specific pain points or blind spots, and the operational or policy questions you most want answered.

Next steps usually include refining the problem statement together, identifying candidate sites or phases, confirming integration points, and outlining a deployment plan. From there you can determine whether to move forward with a pilot, a specific zone, or a broader rollout.

If you have questions that are specific to your environment, you can share a brief description of your assets and current stack, and the team will set up a session that addresses those questions directly.