How Engineers Solve Navigation and Routing Challenges with Traffic Data

Building navigation applications that users trust requires solving hard problems: detecting incidents before users hit them, calculating accurate arrival times, finding genuinely better alternate routes. This guide covers the technical approaches engineering teams use to solve these challenges.

Challenge 1: Rerouting Too Late

The Problem: Users get reroute suggestions after they've already encountered congestion. By the time the app knows about an incident, the user has already passed the last good exit.

The Solution: Reduce detection latency by integrating faster data sources. Camera and dashcam AI provide sub-10-second detection. Telematics hard brake events detect in 30-60 seconds. Use the fastest available source for initial detection, then enrich with slower verified sources.

Implementation approach:

• Subscribe to real-time WebSocket feeds for active navigation sessions
• Implement corridor-based subscriptions along the current route
• Trigger reroute evaluation immediately on any incident detection
• Calculate time-to-incident vs. time-to-reroute for each alert

Challenge 2: Inaccurate ETAs

The Problem: Predicted arrival times don't match reality. Users learn to ignore ETAs or add their own buffer time, eroding trust in the application.

Root Causes:

• Stale speed data used for travel time calculation
• Incidents not factored into ETA until user is already delayed
• Historical averages used when real-time data is sparse
• Signal timing and turns not modeled accurately

The Solution: Combine real-time speed data with incident-aware delay modeling. When an incident is detected on the route:

• Estimate delay based on incident severity and historical patterns
• Update ETA immediately, don't wait for speed data to reflect delay
• Monitor actual speeds through incident zone and adjust estimate
• Learn from historical clearance times for similar incidents

Challenge 3: Missing Incidents

The Problem: Users encounter incidents that the app didn't know about. Even worse, this often happens on routes the app suggested, destroying user trust.

Root Cause: Single-source traffic data has coverage gaps. GPS probe coverage is sparse in rural areas. Camera coverage is limited to urban highways. Crowdsourced reports depend on other users seeing and reporting the incident.

The Solution: Multi-source aggregation that provides overlapping coverage. When one source has a gap, another fills it.

Implementation requires understanding where your current coverage is weak and adding sources that address those gaps specifically.

Challenge 4: Bad Reroutes

The Problem: The app suggests an alternate route that ends up being slower than staying on the original route. Users get "rerouted into traffic" and blame the application.

Root Causes:

• Incident severity overestimated, original delay was minor
• Alternate route had its own congestion not reflected in data
• Reroute triggered on low-confidence detection (false positive)
• Route stability not considered (reroute into another reroute)

The Solution: Implement smart reroute decision logic:

1. Confidence thresholds: Only reroute for high-confidence detections or multi-source corroborated incidents
2. Time savings threshold: Require meaningful savings (5+ minutes) before suggesting alternate
3. Alternate validation: Verify the alternate route doesn't have its own incidents
4. Stability period: Don't reroute again within X minutes unless new major incident
5. Visual confirmation: Weight camera/dashcam detections higher for severity assessment

Challenge 5: Poor Data Quality

The Problem: Traffic data has errors—false positive incidents, stale data showing cleared incidents, incorrect severity assessments. Bad data leads to bad routing decisions.

The Solution: Implement data quality layers:

Freshness Validation

Track timestamps and discard stale data:

• Speed data older than 10 minutes: fall back to historical
• Incidents without updates for 30+ minutes: reduce confidence
• Sources with no heartbeat: mark as potentially down

Cross-Source Validation

Corroborate detections across sources:

• Camera + telematics agreement: high confidence
• Single-source detection only: medium confidence
• Contradicting sources: investigate before routing impact

Feedback Loop

Track prediction accuracy and adjust source weighting:

• Monitor how often predicted incidents affected users
• Track ETA accuracy by segment and source
• Identify sources with high false positive rates

The Common Thread: Better Data

Every navigation challenge we've covered traces back to traffic data quality:

• Late rerouting → slow data
• Inaccurate ETAs → incomplete incident data
• Missing incidents → coverage gaps
• Bad reroutes → poor severity assessment
• Data errors → single-source reliance