Increase on-site accuracy with customizable tracking and detection logic

In logistics, accuracy matters.

Whether you're tracking a delivery driver's arrival, confirming a technician clocked in at the right job site, or monitoring handoffs in a warehouse, getting location wrong costs time and trust. Missed detections lead to disputes. False positives create noise. Poor accuracy means more manual work and less confidence in your system.

At Radar, we help logistics teams take control of on-site accuracy with customizable tracking and detection logic. This gives you better results with less guesswork.

The challenge with default tracking settings

Most location SDKs ship with default tracking behavior. But real-world logistics is complex. You might need tight geofences for a small warehouse, or looser boundaries for outdoor worksites. A delivery app may need high-frequency updates during a route, while a shift tracking app may only care about precise check-in and check-out events.

One-size-fits-all tracking doesn't work across:

• Urban vs. rural environments: GPS behaves differently near tall buildings or in areas with poor signal.
  
• Indoor vs. outdoor use: Altitude and sensor data matter more when detecting which floor or section someone is on.
  
• Different job types: A technician on a long shift behaves differently from a gig worker doing 10 stops per hour.
  

That is why Radar gives you fine-grained control over how location is tracked and how geofences are evaluated.

Custom tracking options in the Radar SDK

The Radar SDK supports multiple tracking presets, each designed for different use cases:

• Responsive: Balances precision and battery usage. Ideal for apps that need real-time updates when a user is moving.
  
• Efficient: Prioritizes battery life over precision. Great for background use or shift apps that only need basic check-ins.
  
• Continuous: Maximizes precision, even when the app is in the background. Designed for high-accuracy use cases like turn-by-turn tracking.
  
• Custom: Allows full control over tracking intervals, desired accuracy, and activity type.
  

Choosing the right tracking mode helps you strike the right balance between accuracy, battery usage, and data granularity. For example:

• A field service app might use efficient mode with tight geofences to detect job site arrivals and departures.
  
• A delivery app could switch to continuous mode during active trips, then back to responsive when idle.
  
• A shift tracking app might use custom mode to check every few minutes near expected shift start times.
  

You can configure tracking behavior remotely using remote config, which means you do not need to ship an app update to tune performance later.

Configurable detection logic

Radar doesn't just give you control over how location is tracked. It also gives you control over how and when geofences are triggered.

For logistics use cases, this flexibility helps reduce false positives and missed events, especially when workers are near the edge of a job site or quickly passing by.

Here are a few ways to improve detection accuracy:

• Stop detection: Enable stop detection at the geofence or project level to trigger an entry only when a user has stopped inside the geofence. This helps filter out quick drive-bys or short visits.
  
• Custom stop settings: Configure parameters like stop duration and stop distance to define what counts as a stop. For example, you can require that a user remains within 100 meters for at least 3 minutes before an entry is logged.
  
• Buffered entries: This setting is on by default. It allows entry events when the device's accuracy circle overlaps with a geofence. You can turn it off to require a more precise location match.
  
• Buffered exits: Use this setting to prevent early exit events when a user briefly steps outside the geofence. This is helpful for avoiding false check-outs when someone is still effectively on-site.
  
• Location accuracy filter: Set a minimum required accuracy for location updates. Locations that fall below this threshold will be ignored, reducing noise from unreliable signals.
  

With these tools, you can fine-tune detection logic to better match real-world behavior. You can avoid false check-ins from nearby roads or parking lots, prevent early check-outs if someone briefly steps outside the boundary, and adjust detection behavior to suit different site layouts, shift patterns, or route types.

Radar gives you the control to adapt geofence detection to your specific logistics needs, all without custom-built logic.

Real-world examples

Here's how logistics teams are improving accuracy with Radar.

Milk Moovement: Smarter driver and delivery tracking

Milk Moovement helps dairy cooperatives manage pickups, deliveries, and driver schedules. Before Radar, their team relied on a homegrown system that struggled with location accuracy and scale.

Now, with Radar powering their location tracking, Milk Moovement can detect when drivers arrive at farms or processing facilities with greater precision. This helps reduce manual check-ins, keeps schedules on track, and gives their operations team real-time visibility into what is happening in the field.

See how Milk Moovement uses Radar.

Turvo: Visibility at every point in the supply chain

Turvo connects shippers, carriers, and brokers through a collaborative logistics platform. They use Radar to improve tracking across shipments, warehouses, and delivery sites.

By using Radar to detect arrival and departure events at key locations, Turvo helps logistics teams stay ahead of delays, shorten wait times, and create a more seamless experience for customers and drivers alike.

See how Turvo uses Radar.

Other examples

• Shift tracking: A workforce management app uses Radar to detect when employees start or end shifts at job sites, helping reduce no-shows and confirm time on-site.
  
• Field service: A service company uses Radar to track technician visits across multiple sites per day, avoiding missed entries or false check-ins.
  
• Delivery: A last-mile delivery team uses Radar to track arrivals at dense stop clusters, helping optimize routes and improve delivery confirmation accuracy.
  

Many teams rely on Radar to reduce location errors, automate workflows, and provide reliable data without increasing manual overhead.

How to get started

Getting started with Radar takes just a few steps.

1. Sign up for Radar
Create a free account at radar.com and get your API key.

2. Create geofences
Define geofences in the Radar dashboard or via the Geofences API. You can set detection settings like stop detection or buffered entries at the geofence or project level.

3. Integrate the SDK
Install the iOS or Android SDK, then call either Radar.trackOnce() or Radar.startTracking() depending on your use case.

Use trackOnce() for single-point checks like clocking in or confirming a delivery.

Use startTracking() for continuous tracking across a shift, route, or job.

Radar will generate an entry event on the first valid location update inside a geofence. You can view these events in the dashboard or receive them via webhook.

For full setup instructions, visit the Radar docs.

Better accuracy, less overhead

Getting location wrong creates overhead for your team, your ops, and your customers.

Customizable tracking and detection logic helps you build trust with your users, reduce disputes and manual resolution work, and improve visibility and automation across your operations.

Radar gives you control where it counts, at the point of check-in, the moment of arrival, the edge of the geofence.

Ready to get started? Explore the docs or book a demo to learn how Radar can power high-accuracy logistics tracking for your team.