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How accurate are HERE ETAs?

Short answer: Accurate enough that the routing engine is rarely your problem. Your ETA is wrong because a routing duration is not a delivery promise.
Presenting a routing duration as an ETA is the most common source of ETA inaccuracy — and it is a modelling error, not an API error. It survives migration.

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What the routing engine doesn’t know

A routing duration is the time a vehicle spends moving. That’s rarely the number the customer cares about. Missing:
  • Preparation time — food cooking, order picking, loading
  • Service time at the stop — unloading, signature, elevator, the customer who takes four minutes to answer
  • Dwell time between arrival and next departure
  • Parking — eleven minutes in dense urban delivery, modelled by nobody
  • Driver behaviour — breaks, deviations
In dense last-mile delivery, service time and failed attempts frequently dominate travel time.

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Decompose it

ETA = preparation + travel + service + buffer Store each separately. Present the sum.
Instrument the residual — actual minus predicted — broken down by component.Most teams discover that travel duration is accurate to within a few percent and service time is off by 40%. That tells you where to spend the next quarter.

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Set departureTime explicitly

Traffic is evaluated at the departure time, not at request time. A route computed at 2pm for a 6pm departure inherits 2pm traffic unless you say otherwise. departureTime=any disables traffic awareness entirely.

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Don’t re-route to refresh

Re-routing on every GPS ping to refresh an ETA is the same architectural error as reverse-geocoding every ping.Recompute the remaining duration from the route geometry and current position. That’s arithmetic on data you already hold. Call routing when the driver deviates.

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Communicate uncertainty

“Arriving at 3:14pm” is a promise you cannot keep. “Between 3:05 and 3:25” is one you can. Report the residual distribution — p50, p90, p99 — not the mean. A platform whose durations are unbiased on average but have twice the variance produces twice as many late deliveries.

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Common misconceptions

“Google’s traffic is better, so their ETAs are better.” Google’s traffic derives from an enormous consumer device population. That’s a real advantage for dense urban passenger routing. Whether it’s an advantage on your corridors, at your departure times, is empirical. Test it. “Switching vendors will fix our ETAs.” If service time is your error term, no vendor helps. “Multi-stop ETAs compound linearly.” They compound at every hop, and dwell time compounds with them. “A car route’s duration is a truck’s duration.” It isn’t, because it isn’t the truck’s route.

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Build the feedback loop

Actual arrival minus predicted, per job type, per time of day, fed back into service-time estimates. Without it, your ETA accuracy will never improve — on any platform.

ETA Calculation

Decomposition, the feedback loop, and cost traps.

Last-Mile Delivery

Where service time dominates travel time.

Fleet Dashboard

ETA refresh from geometry, in code.

Routing

departureTime and traffic semantics.
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