ParkingAid Prediction API

Name: ParkingAid Prediction API
Brand: ParkingAid Prediction API
Availability: InStock

✓ Official Vendor SpecTransportationParking Analyticsbearer1 EndpointsREST

For Agents

Get minute-by-minute parking availability predictions for locations based on historical data and ML forecasts.

Use for: I need parking availability forecast for downtown at 3pm, Check if parking will be available near the stadium in 2 hours, Get parking predictions for a radius around my destination

Not supported: Does not reserve parking spots, process payments, or control parking hardware — use only for obtaining availability forecasts based on historical and real-time data.

Install Jentic One Beta

Connect the ParkingAid Prediction API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the ParkingAid Prediction API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with ParkingAid Prediction API.

Retrieve parking availability predictions for a geographic location with configurable radius

Specify forecast timeframes such as 2h, 4h, or custom durations

Receive minute-by-minute availability scores for nearby parking blocks

GET STARTED

Start building with ParkingAid Prediction API

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View OpenAPI Document

Use Cases

Patterns agents use ParkingAid Prediction API for, with concrete tasks.

★ Pre-Trip Parking Planning

Integrate ParkingAid predictions into navigation or ride-hailing apps to help users plan arrivals around parking availability. POST /v1/predictions with the destination coordinates, radius, and departure time offset (as timeframe) returns a forecast of availability scores. Users can adjust departure times or choose alternate locations based on predicted ease of parking.

POST /v1/predictions with location (lat, lng, radius), timeframe (e.g., '2h'), and include_confidence=true. Parse the blocks array and present the availability_score for each block in the response.

Event Parking Guidance

For event venues or stadiums, provide attendees with predicted parking availability windows. Query the API before event start time to surface high-confidence blocks with favorable scores, reducing circling and congestion. Embed predictions in event confirmation emails or venue apps.

POST /v1/predictions with the venue coordinates, a 500m radius, and a 3h timeframe covering the event window. Filter blocks by availability_score > 0.7 and confidence > 0.8 for reliable guidance.

Key Endpoints

1 endpoints — parkingaid prediction api provides minute-by-minute parking availability forecasts for specified locations using machine learning models trained on historical occupancy data.

METHOD

PATH

DESCRIPTION

POST

/v1/predictions

Retrieve parking availability predictions for a location

POST

/v1/predictions

Retrieve parking availability predictions for a location

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

ParkingAid bearer tokens are stored encrypted in Jentic's vault and injected as the Authorization header at execution time without exposure in prompts.

Intent-based discovery

Agents search by intent such as 'parking availability forecast' or 'parking prediction near location' and Jentic returns the predictions endpoint with its schema and example requests.

Time to first call

Direct ParkingAid integration: 1 day to model the prediction request/response. Through Jentic: under 1 hour — search, load schema, execute.

FAQs

Specific to using ParkingAid Prediction API through Jentic.

What authentication does the ParkingAid API use?

The API uses bearer token authentication. Jentic stores the bearer token encrypted and injects it as the Authorization header at execution time.

How accurate are the parking predictions?

Accuracy depends on the historical data density for the queried location and the confidence score returned per block. Higher confidence scores indicate more reliable predictions. Include confidence=true in the request to assess reliability.

What units are used for radius and timeframe?

Radius is specified in meters. Timeframe is a duration string like '2h' (2 hours) or '30m' (30 minutes). The API returns predictions covering the specified duration from the current time.