Wialon already knows when a vehicle was braked hard, accelerated hard, taken through a turn too fast or driven over the limit. The eco-driving criteria on the unit define what counts, and each carries a penalty weight. What the platform gives you back is a list of violations.
A list is not a management tool. Nobody schedules a coaching session off a list of 4,300 events, and nobody compares a long-haul driver to a city courier by counting rows. The step from violations to a score somebody will act on is where most eco-driving programmes stall, and it comes down to four decisions.
Decision one: what you divide by
A raw violation count punishes whoever drives the most. Five harsh brakes over a 1,000 km week is a different driver from five over 80 km of city delivery, and any ranking that does not know the difference gets ignored by the people it ranks.
So a score is violations per unit of exposure, and there are two honest choices:
- Per distance. The default for mixed fleets, and the one most drivers accept intuitively.
- Per duration. Better where the work is slow and dense — urban delivery, municipal services, anything that spends its day below 30 km/h, where distance flatters nobody fairly.
Pick one and keep it: switching the denominator changes everyone’s ranking, and a ranking that moves for reasons drivers cannot see is one they stop trusting.
Decision two: which criteria count
Wialon lets you define criteria freely, and fleets accumulate them. Not all of them belong in a score. A criterion that fires constantly because it was tuned for a different vehicle class adds noise, not signal; one that never fires adds nothing at all.
The useful discipline is to score on the handful you would actually coach someone about — harsh braking, harsh acceleration, sharp turns, speeding — and leave the rest visible but excluded. In FleetTAB that is a selection: you tick which criteria feed the score, and the violations you excluded stay in the list for anyone who wants to look.
Decision three: how hard a violation hits
Wialon’s criteria carry penalty weights, and the raw numbers are rarely on the scale you want. A fleet whose weights were set for a different purpose ends up with every driver at zero, which is as useless as every driver at 100.
The fix is a multiplier applied on top of Wialon’s weights — ×1, ×0.1, ×0.01, ×0.001 — that rescales the whole fleet at once without touching the platform’s configuration. You are not changing what counts as a violation; you are changing how steeply the score falls when one happens. Set it so that your median driver lands where you want the conversation to start, usually somewhere in the 70s or 80s.
Then the thresholds: what counts as excellent, good, average, poor. Defaults of 95, 80 and 60 work for most fleets, and there is nothing sacred about them. What matters is that the bands mean something operationally — “excellent” should be a standard a careful driver can reach, not a theoretical maximum.
Decision four: the driver, not the vehicle
A vehicle score tells you how a van is being driven. A driver score tells you who is driving it that way, and only one of those supports a conversation.
The bridge is Wialon’s driver assignment history. Each violation is attributed to whoever was bound to the unit at the moment it occurred, and the distance for the period is split between drivers in proportion to how long each one was bound. That gives you both views from the same data: one row per vehicle, and one row per driver, with the same violations counted once.
Which means driver bindings matter more than most fleets think. A fleet with sloppy bindings gets vehicle scores and nothing else — and the fastest way to fix that is bulk assignment over a filtered selection rather than vehicle-by-vehicle, which is exactly what the driver logbook is for.
Make the score openable
The difference between a scoring system that changes behaviour and one that generates arguments is whether the number opens.
A driver who is told “you scored 62” has been given a verdict. A driver who can see that the 62 came from eleven harsh-braking events, nine of them on the same stretch of road on the same two days, has been given something to talk about — and sometimes an explanation you needed to hear, because a road being resurfaced produces harsh braking that no coaching will fix.
So: every score opens into its violations, each violation carries its time and place, and the scoring settings are visible to the people being scored. A fleet that publishes its scoring criteria gets argued with about the criteria, which is a much better argument than the one about whether the numbers are fair.
What to do with the result
Three uses, in order of how quickly they pay off:
- Coach the tail. The bottom five drivers in a fleet produce a disproportionate share of the hard events, the brake wear and the fuel burn. They are also the easiest to improve, because the behaviour is usually habit rather than conditions.
- Watch the trend, not the rank. A driver improving from 55 to 70 is the outcome you wanted. A leaderboard rewards the drivers whose routes were always easy.
- Check the roads. When several drivers collect the same violation in the same place, the place is the problem. That is a routing decision, not a personnel one.
Where FleetTAB fits
Eco-driving in FleetTAB is Wialon’s own detection with the four decisions above made explicit: which criteria count, what you divide by, how steep the penalty is, where the colour bands sit — all editable, all applied to driver and unit scores computed from the same events. The scores sit next to the trips they came from, so a bad week opens into the drives that made it bad.
The eco-driving documentation covers the settings in detail, and the module is part of the free application on your own Wialon account.
