AI‑Powered Smart Wayfinding

Custom Maps and Intelligent Wayfinding Solutions. Mapiddiction's navigation engine, powered by AI and machine learning, delivers more than just directions — it creates intelligent, customisable routes that adapt to real-world conditions, offering smarter guidance within private maps.

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AI wayfinding map example

AI WAYFINDING

Why Machine‑Learning Routing Beats Traditional Shortest‑Path

Traditional routingML/AI‑powered routing
Static edge weights set by humansDynamic weights predicted from real usage, time, density and feedback
Same route for every travellerPersonalised to mobility level, transport mode and (soon) user role
Requires manual tweaking to add new rulesContinuously learns from new data—no redeploy needed
No awareness of time‑based closures or crowdingContext tags (e.g. "night", "peak", "service‑only") steer users automatically
Limited insight for adminsFeedback analytics flag bottlenecks and dead zones

Benefits

Faster & safer journeys

Avoids stairs for wheelchair users, prefers well‑lit paths at night.

Lower maintenance

The rule engine + Random Forest weight predictor tune themselves from data.

Future‑proof

New transport modes or map constraints drop in as just another feature column.

Actionable insights

Heat‑maps and satisfaction scores surface where your map needs attention.

How the Engine Works

1

Rule Engine

Checks obvious constraints first (e.g. handicap → no stairs).

2

Weight Predictor

Blends 70% baseline weight with 30% ML weight to finesse route choices.

3

Training Pipeline

Retrains on fresh route logs, feedback & context every night.

4

Evaluation System

Tracks accuracy, user satisfaction and API latency so we only ship improvements.

Wayfinding engine

ROADMAP

What’s Next

Our journey to smarter wayfinding continues. Explore the phases that will shape the future of intelligent navigation.

Available Now

Phase 0 – Initial Launch

  • Modes: Walking, Bike, Drive, Disabled
  • Base ML engine + ruleset active
  • API integration ready
Coming Soon

Phase 1 – Context Tags

  • Time‑of‑day routing (day/night)
  • Density‑aware detours
  • "Avoid stairs" for accessibility
Coming Soon

Phase 2 – Preference Profiles

  • Scenic vs Shortest toggle
  • Lift‑first preference
  • Advanced Protected zone avoidance
Planned

Phase 3 – Continuous Learning

  • Nightly model retraining
  • Feedback UI integration
  • Quality dashboard
Planned

Future Phases

  • Real‑time obstacle feeds
  • Public transport integration
  • Voice guidance & multilingual support
  • Predictive intent routing