Parking occupancy software without the sensor mesh

Anyone who runs more than thirty parking spaces and cares about revenue benefits from real-time occupancy data. Below thirty spaces a glance out the window is usually enough. Above thirty spaces — and certainly at hundreds or thousands — the difference between “the lot feels busy” and “the lot is 87% full and forecast to peak in 22 minutes” is the difference between competent operations and revenue left on the table.

The classic buyers of parking occupancy software have been airports, hospitals, and municipal operators. They paid for sensor meshes and dashboards because no other option existed. The buyers we see today include surface-lot owners who never could have justified a $50,000 sensor install, mixed-use developers who want a fullness signal across an entire campus, and event venues that need a live count once a week. Park Graph is built for all of these because it does not require capex.

Every Park Graph paid session has a known start time, end time, and lot. The platform computes occupancy at any timestamp T as the count of sessions whose start ≤ T and end > T, divided by the lot capacity. This is exactly the same arithmetic a sensor system performs on entry/exit events. The only difference is that the source signal is the payment, not the sensor.

For lots where 100% of drivers pay (most paid surface lots), payment-derived occupancy and sensor-derived occupancy converge to within a few percent. The remaining gap comes from monthly permit holders, validations, and grace-period entries. Park Graph reconciles these by recording a license-plate or QR-scan fingerprint at the entry event so the count remains accurate even when no payment changed hands.

Where you need exact non-payment occupancy — for example a free lot, or a lot with a high mix of permit holders — Park Graph integrates with sensor and LPR feeds through the API. The dashboard merges payment and sensor signals automatically and surfaces discrepancies (a sensor entry without a payment is flagged for review).

The occupancy tile in the dashboard preview above shows current fullness, a 24-hour spark line, and the forecast for the next 6 hours. Operators with multiple lots see a consolidated heatmap; operators with a single lot see a per-zone breakdown. The same data is available through the public API and through the Park Graph MCP server for AI agents.

1. 1

   Create the lot

   ~4 min

   Add the lot in the dashboard with its capacity (the denominator for occupancy).

2. 2

   Start collecting paid sessions

   ~5 min

   Print and post a QR code. Every paid session feeds the occupancy counter.

3. 3

   Verify the dashboard

   ~2 min

   Confirm the occupancy tile updates as test sessions are created.

4. 4

   Optionally connect sensors

   ~30 min

   Add a sensor or LPR feed via the integrations menu if you need exact non-payment occupancy.

5. 5

   Publish to maps

   ~1 min

   Toggle public availability so Google, Apple, Waze, and AI agents see live occupancy.

6. 6

   Set occupancy-driven pricing rules

   ~5 min

   Configure rate steps at, e.g., 70% and 90% fullness.