Quantifying Delay from Route Deviation (Quantifying Delay From Route Deviation)

Quantifying Delay from Route Deviation

Delay is not only traffic: preferred-path detours create measurable ETA error for dispatchers.

ETA baseline vs actual arrival

Gecikme KPI’sı sefer UI’sine bağlanır.

Concepts, defined where they first appear

📦 OSRM
Open-source road-network shortest-path engine used as the recommended-route baseline.

📦 Preferred Path
The path drivers actually take — recovered from telemetry traces.

📦 Km Extension
Extra kilometers of the preferred path versus the OSRM baseline.

📦 Wait Hotspot
Geographic cluster where waiting events concentrate.

Blurring these layers produces the wrong truth in analytics and on the map UI.

Shape of the problem

Delay is not only traffic: preferred-path detours create measurable ETA error for dispatchers.

ETA baseline vs actual arrival

The split that works

Gecikme KPI’sı sefer UI’sine bağlanır.

ETA baseline vs actual arrival
        ↓
   explicit contract → frontend

Frontend bridge

Ops maps, wait-geofence UI, and MFEs consume these read models — they never bind to raw ERP/MySQL.

The mappings that get confused most often

❌ ERP MySQL = analytics database
✓ ERP is a write store; analytics lives in PostGIS

❌ Let the map trust public/vendor routers blindly
✓ Production needs your API/OSRM edge

❌ Log ML outputs with PII
✓ Evaluation and serving obey privacy boundaries

A checklist for auditing your own system

1. Which store is source of truth for this surface?
2. Which API contract does the frontend call?
3. What happens if one cluster node dies?
4. How is lag shown to operators?
5. Deny-list: any client domain/vendor leakage?

What to take away from this part

1. Polyglot stores exist for different workloads; one-DB fantasies are fragile.
2. Frontends consume clean read models, not the ERP.
3. ML/OSRM outputs need an API contract before they hit the map.

A beautiful map drawn from a dirty source is still a lie.