Spatial intelligence: what the whole fleet learns about the road

What this chapter covers

A single truck's crossing tells you about that truck. Thousands of crossings, pooled, tell you about the road — where every truck slows down, where they all stop, and where an active incident is blocking the way today. This chapter is about the three kinds of knowledge Korido builds from the whole fleet's traces: slowdown zones (the road is chronically slow here, and by roughly this much), hotspots (trucks repeatedly stop or struggle here), and anomalies (an incident is disrupting the corridor right now). It explains how each one feeds the ETA indicator, paints the live map, and — crucially — how each one ages out when conditions clear.

Because a steep climb or a border queue is a property of the road and not of any one fleet, these three entities are cross-tenant: two operators watching the same physical place see the same knowledge.

The picture

All three are built the same way — many small observations from many trucks, clustered into one named thing that carries an expected time cost:

Slowdown zones: the road is slow here

Whenever a truck moves consistently below the corridor's expected speed, that stretch is recorded as a slowdown observation — a single midpoint on the road, kept as a point for cheapness rather than a full traced path. On a schedule, Korido clusters those midpoints into slowdown zones: typed, recurring slow sections of road.

• What a zone is. A cluster of slow observations turned into one shape — a circular buffer around the cluster's centre of mass — with a kind: mountain_climb, mountain_descent, urban_congestion, rough_road, border_queue, roadwork_recurring, or sharp_curve.
• Direction matters. The same physical road can be slow uphill and fast downhill, so a zone carries a bearing class and the detector emits separate zones per direction when the speed distributions diverge. Mount Cameroon's climb is a zone; its descent is a different one.
• The expected time cost. Each zone carries an expected drag in seconds — how much longer a truck takes because of the zone — computed from the gap between the zone's observed median speed and the corridor baseline, scaled by how much of the route overlaps the zone. A calibration schedule keeps this number current from fresh observations. It is capped at a sane ceiling so a detector glitch can never add days to an estimate.
• Confidence. A zone carries a confidence between 0 and 1 that reflects how much evidence stands behind it, and per-hour, per-day, per-season, per-weather breakdowns so the drag can be read in context.

Hotspots: trucks stop or struggle here

Where slowdown zones are about slow motion, hotspots are about stopping and struggling. Korido clusters the fleet's stop records by place and event class into named hotspots, each with an expected dwell time — how long trucks typically wait there.

• What a hotspot is. A recurring cluster of one event class: weighbridge, border_post, customs, toll_gate, fuel_station, rest_area, meal_stop, commerce, checkpoint, roadblock_recurring, breakdown_zone, speeding_zone, harsh_braking_zone, or signal_loss. A hotspot is a place — a weighbridge is the same weighbridge for every fleet, which is exactly why hotspots are cross-tenant.
• Expected dwell. A hotspot holds median (p50) and near-worst (p90) dwell times, bucketed by hour of day, day of week, season, and calendar, with an overall baseline as the fallback when a bucket is thin. The route estimator reads the right bucket for the time a truck will actually arrive.
• Curation. The detector finds candidate hotspots and leaves them unnamed; an operator reviews and gives a hotspot a display name. A well-observed tenant hotspot can be promoted to a verified global one that every fleet benefits from. Sub-places nest — a fuel station inside a city groups under the city.

Anomalies: something is wrong on the corridor now

Zones and hotspots are about the road's permanent character. Anomalies are about today: a strike, an accident, a bridge or road closure, a flood, civil unrest, a new checkpoint, a fuel shortage. They also cover tenant-private patterns — an unauthorised-stop pattern, a fuel-loss pattern, a driver-behaviour pattern — which stay private to the fleet that owns them.

• Mixed visibility. An anomaly is either cross-tenant — a border closure is visible to every operator — or tenant-private. A single table holds both, and a strict rule keeps them straight: cross-tenant anomalies have no owner, private ones always do.
• Shape. An anomaly's geometry is whatever fits the incident — a point for an accident, a polygon for a flood area, a line for a road closure.
• Expected delay. Like zones, an active anomaly carries an expected drag — the extra seconds a route crossing it should expect. When the detector cannot model the impact, the estimator falls back to a conservative default rather than assuming zero.
• The "blocks the road" flag. An operator can mark an anomaly as fully blocking the corridor — a closed border, an impassable road, a customs strike. This flag is decisive: for any mission whose remaining route crosses that geometry, the ETA schedule stops producing a number and instead reports a blocked confidence with a frozen arrival — because there is no honest estimate for a truck that physically cannot proceed.
• Severity. Anomalies carry info / warning / critical severity, which routes them into the alert system described in Alerts.

Feeding the ETA indicator

Korido's ETA is layered: on top of a driving baseline, each active piece of spatial intelligence whose geometry the remaining route crosses contributes its own additive term:

The estimator allocates each drag term to the segment it overlaps, so a route's total predicted delay is the honest sum of the specific slow sections, waits, and incidents it will actually meet. A blocking anomaly overrides the sum entirely: no drag arithmetic can turn "the road is closed" into a plausible arrival time.

Painting the map

The same three entities render on the live map so a dispatcher sees the road the way the estimator does: slowdown zones as slow stretches, hotspots as the places trucks habitually wait, and anomalies as active incidents in their real shape — a point, a flood polygon, or a closure line — coloured by severity. Where an anomaly intersects a known hotspot (an incident at a border that already has a customs hotspot), the two are linked so a reader sees one correlated disruption rather than two stacked ones.

Ageing out when conditions clear

Spatial intelligence must forget as well as learn, or the map fills with ghosts — a strike that ended, a rest stop that closed. What makes this genuinely hard is that the three entities are three different kinds of truth, so no single expiry rule fits them all: an anomaly is an incident that ends, a hotspot is a habit that fades, and a slowdown zone is a slow stretch that can flatten out. Each therefore leaves the picture in its own way, and a daily maintenance pass applies the last two.

An anomaly closes the moment its incident clears. An operator stamps the end time, or a detection pass closes a cross-tenant one on its own — a flood subsides, a border reopens. The status becomes resolved (or false_positive if it was never real, or merged if it was folded into another). Once inactive, an anomaly stops contributing drag and drops off both the ETA and the map, and any blocking freeze it held lifts on the same beat.

A hotspot goes dormant when the trucks stop coming. When a place records no fresh stop for 90 days, the daily pass marks it dormant: it stops shaping ETAs and fades from the working map, so a rest area that shut down does not haunt the corridor forever. A hotspot an operator has curated — named and confirmed as a real place — is exempt, because a human has already vouched that it belongs there.

A slowdown zone resolves when the road is no longer slow. Two forces retire a zone. The same daily pass marks a zone resolved after 60 days with no fresh slow observation; and because zones are rebuilt from a rolling window of recent traces, a stretch that has sped up simply stops being re-clustered on the next rebuild. That rebuild works in bounded chunks — a fixed number of map bins per tick — so it stays cheap even as the corridor's history grows.

The result is a picture of the road that reflects the last couple of months of reality — sharp where conditions persist, and quiet where they have moved on.

Edge cases

• A zone that reverses with direction. Climb-and-descent asymmetry is preserved by the bearing class; a single road can hold two zones with very different drags.
• A detector that cannot price an incident. When an anomaly's impact can't be modelled, the estimator uses a conservative default rather than zero, so an unmodelled incident never silently disappears from the ETA.
• An incident on top of a known wait. An anomaly intersecting a hotspot is linked to it so the correlated drag is read once, not double-counted at a border.
• A private pattern. A fuel-loss or driver-behaviour anomaly stays scoped to the fleet that owns it and never appears on another tenant's map or ETA.
• A runaway drag value. Both zones and anomalies cap their expected drag at a hard ceiling, so a bug in detection can never inflate an estimate without bound.
• A blocked road that reopens. Clearing a blocking anomaly restores normal layered ETA on the next refresh — the freeze is a live consequence of the flag, not a permanent stamp on the mission.

Known limitations

• Road knowledge needs traffic before it exists. Slowdown zones, hotspots, and their expected costs are all clustered from accumulated crossings. A brand-new place, a rarely-driven branch, or a corridor in its first weeks carries thin evidence and low confidence, so ETAs there lean on the driving baseline and conservative defaults until enough trucks have passed to sharpen the numbers.
• The picture is deliberately recent. Because zones and hotspots are rebuilt from a rolling window and age out after weeks of silence, a pattern that recurs only seasonally can fall out of the working picture between seasons and be re-learned when it returns. This keeps the map sharp on current reality at the cost of long memory.

What's ahead

The hotspot model already carries more of a lifecycle than the daily maintenance pass writes today. A quiet place is only ever marked dormant; the next steps are to revive a dormant hotspot back to active when trucks start stopping there again, and to archive one that is gone for good rather than leaving it dormant indefinitely — states the model already reserves. In the same direction, Korido will promote more well-observed tenant hotspots into the verified global set every fleet shares, so the corridor's collective memory keeps compounding.

How it connects

• The ETA layers these terms feed are assembled in Progression and ETA.
• The per-crossing analytics that net out this expected drag from real lateness are in Segment analytics.
• Anomaly severities flow into the alert system in Alerts.
• Corridor geometry and how a route is matched against it are covered in Route Guard.