AI cannot reliably predict remaining parking spaces

What drivers need is a live answer: does this parking lot have available spaces now? This is not a simple text-reasoning problem; it is a real-time urban data problem. Parking gates, ground sensors, cameras, payment systems, and map providers are often not connected, and their update frequency, definitions, and reliability vary.

Have AI accurately predict the real-time number of remaining spaces in a specified parking lot before the user arrives, with data source, update time, error range, and confidence.

The hardest part is parking data sharing. Commercial garages, malls, office buildings, residential compounds, street parking systems, and city platforms each control different data. Interfaces are not standardized, many locations expose no API, and even available data may be delayed, incomplete, manually adjusted, or reduced to vague states such as 'available' or 'busy' instead of exact counts.

Current AI can describe historical patterns and make plausible guesses from weekends, districts, weather, and events. But without a real-time, standardized, verifiable data stream, it is still guessing. Actual availability changes quickly because of temporary entries and exits, reserved spaces, monthly-pass spaces, gate failures, manual overrides, and data latency.

A successful case must connect to at least one real parking lot or city parking platform data source and record predictions for at least 7 consecutive days. Each prediction must include timestamp, parking-lot ID, predicted remaining spaces, actual remaining spaces, error, data update time, and screenshot/log evidence. Media reports, static map heat, or post-hoc explanations do not count.