What
That sharp drop in booking conversion, the one that arrives in steps instead of a slow slide, usually tracks back to reconnect events. When a vehicle passes through a tunnel or a dead corridor, the device loses its link and starts storing positions in memory. The moment the network returns, it flushes everything at once. Your platform then receives a cluster of positions carrying old timestamps, and if it treats them as current state, availability flips in ways nobody watching the vehicle would recognise. If that sounds like your pattern, the good news is you can confirm it with data you already have.Start with one hour of raw position logs and one hour of published state logs side by side. Mark each reconnect event on the same timeline. Where booking conversion bursts downward, you should see a matching cluster of stored positions arriving together. The tell is not just volume. It is the timestamps. Replayed positions carry times from minutes or hours earlier, yet they enter the pipeline as fresh updates. Bookings lost in dense urban corridors with tunnel-heavy stretches are the classic case, because brief network loss is routine there and drivers never notice anything happening.The harder diagnostic is separating a genuine reconnect burst from an ordinary corridor dead zone. A dead zone produces a gap, and then normal reporting resumes. A replay produces a gap, then a dense clump of positions that were never published when they were captured. Ask whether the burst contains positions that duplicate or predate the vehicle's current known state. If yes, you are looking at fleet GPS tracker buffering behaviour, not coverage limits. This distinction matters because the fixes live in completely different places.Next, check what your ingest layer does with those late arrivals. Does it drop duplicates before publishing state, or does it pass every buffered point straight through? Many teams discover that duplicate position removal simply is not configured, so old points overwrite new ones and availability briefly reads from stale data. Look at the ordering too. Position replay on reconnect can reorder events, meaning geofence alerts may land after availability writes rather than before. A vehicle that has left a zone can still look bookable inside it, and the booking engine happily offers something that is not there.When you know where the damage happens, you have two places to fix it. The first is the device side. Review the buffer window and the replay policy on your units, and confirm what firmware actually does under brief loss. Some devices burst everything; others send only the latest point. The second is the platform side, and for most fleets this is where the work pays off. Gate incoming positions by timestamp so anything older than the current published state is discarded. Suppress duplicates on a stable key. Keep real-time state separate from raw telemetry so a replay surge cannot throttle availability lookups. That separation is the same structural idea that shows up in the tune, reconfigure, redesign, or replace decision boundary, and reconnect bursts usually land in the reconfigure column rather than the replace column.A short field list helps. Instrument reconnect events so they appear in logs rather than hiding inside normal traffic. Measure your replay window per fleet, since it varies. Test duplicate suppression with real burst data. Verify event ordering after a replay, especially for geofence alerts that feed availability freshness. Then re-measure the freshness gap the same way you did at the start and see whether booking conversion bursts flatten out. Correlating downtime or restart timing against the gap is also useful evidence when you need to justify a pipeline change to whoever controls the budget.One honest limit. Buffering behaviour differs across firmware versions and platforms, and a replay that looks identical in two fleets can come from different causes. We would not hand you a universal number, because the correct replay window and dedupe rule depend on your corridors, your booking window, and your own measurements. Treat every figure here as something to replace with your own, and validate the fix against the specific replay pattern your logs show.