AI and travel-time prediction (ETA): myths and realities

What are we talking about?

An ETA (Estimated Time of Arrival) is the predicted arrival time at each stop. Computing it requires two building blocks: good sequencing of stops (the routing problem, NP-hard, solved with heuristics) and good duration estimation for travel and service. "AI" mostly acts on the second.

The crystal-ball myth

No model "predicts the future". A reliable ETA combines historical data (observed durations by slot and zone), real-time conditions (traffic) and realistic margins. Be wary of minute-perfect accuracy claims: in the field, an unexpected unload or a red light is enough to shift the chain. The goal isn't perfection — it's an honest, continuously updated range.

What makes a good ETA

• Service durations measured by customer type, not a single average;
• Real rather than theoretical traffic taken into account;
• Continuous re-estimation as the route progresses;
• Transparent communication to the customer (a window, not an exact time).

Why it matters economically

Optimisation and telematics tools form a fast-growing market — fleet management is estimated at about $27 billion in 2025, with annual growth of around 16.9% according to industry analyses. That momentum reflects a simple reality: estimating better means promising better, hence fewer failures and customer calls.

The dropfleet approach

dropfleet favours transparency: an ETA builds on optimised sequencing and real-time tracking, and is communicated to the customer as a window, not false precision. A keepable promise beats a shiny one.

Honest ETAs, not marketing. Try dropfleet free for 14 days — no credit card, ready in 5 minutes.