Commercial Vehicles Experience Cities Differently
Almost every major European city has a congestion figure attached to it. Very few of them describe the experience of the vehicles that keep the city supplied.
Conventional congestion indices are built around the average car journey: a commuter, driving a familiar route, at a predictable time, with no reason to stop between origin and destination. Commercial vehicles do none of this. Vans and trucks follow different routes, operate under different restrictions, make multiple stops, load and unload, and spend a substantial share of their working day stationary by design.
This matters because urban freight and services are among the most heavily regulated activities in European cities. Low-emission zones, loading windows, access restrictions, kerbside reallocation and traffic-calming schemes all land directly on commercial operators – and all are evaluated against evidence drawn from general traffic, which is dominated by cars. A scheme can reduce average car delay while lengthening a delivery round. A van is affected less by mainline speed than by whether it can stop near the delivery point; a truck less by peak-hour flow than by turn restrictions and access windows.
So when a city announces that traffic is improving, the question that actually matters to a fleet operator goes unanswered: is it getting easier or harder for commercial vehicles to get work done here?
The Commercial Trip Monitor
The Webfleet Commercial Trip Monitor, developed by Webfleet in collaboration with Mobito, was built to answer exactly that question. Launched on 15 September 2026 at IAA Transportation in Hannover, it is a free, monthly-updated benchmark covering 32 major European cities across seven markets, built entirely from in-vehicle telematics generated by working commercial fleets.
The two organisations developed the methodology and the data processing behind it together. Webfleet contributes one of Europe’s largest commercial vehicle data footprints and 25 years of experience working with European fleet businesses. Mobito contributes the methodology and analytical layer that turns that footprint into a consistent benchmark – one that can be published monthly, compared across cities, and used with confidence by operators, analysts and transport authorities.
Where Mobito Comes In
Holding a large mobility dataset and being able to publish a defensible monthly figure from it are two different problems. The second is the one Mobito’s work addresses, and it is harder than it sounds.
The requirement is comparability. A published figure is only worth anything if a movement in it means the traffic changed – not that the sample shifted underneath it. Raw commercial vehicle data moves constantly for reasons that have nothing to do with road conditions: different customers, different vehicles, different jobs, a quiet week, a one-off route that never repeats. Left unaddressed, that variation swamps the signal entirely. Our models resolve it, reducing millions of journeys to a consistent, quality-controlled basis for measurement that is then held steady from one month to the next, so the same thing is measured every time.
Seasonality illustrates why this matters. Readings improve sharply across the panel in August, reflecting the strong seasonal effect of lower urban activity. Spain, for example, recorded 8.81 km of 15-minute reach in August 2025 against 7.76 km that October. Any claim that a city has become easier to operate in, based on a single month, is quite likely to be measuring the holidays. Only a measurement held constant across a long enough series makes a year-on-year statement about a city possible at all.
The metrics also have to mean what they say. Idling intensity would be worthless if it counted the minutes a van spends parked while the driver makes a delivery; it is built to isolate genuine traffic friction from the ordinary business of doing the job. Privacy is built into the analytical pipeline in the same way, with pseudonymisation, aggregation and safeguards designed to prevent individual vehicles or drivers from being identified.
What this produces is not a study but a measurement system: the same panel, the same definitions, the same checks, reproduced every month on a series now running since January 2025.
The Two Metrics
15-Minute Reach records how much ground a commercial vehicle covers in a quarter of an hour on the road. It is an indicator of productive capacity – how many drops, jobs or collections a vehicle can complete in a shift.
Idling Intensity records the share of active working time a vehicle spends stationary with the engine running. It is an indicator of waste: fuel burned, emissions produced and paid driver hours consumed without output.
Together they describe the two distinct ways urban conditions add cost to a commercial operation – by slowing the vehicle down, and by leaving it stopped with the engine on. Neither is a verdict on its own; it is when the two move together that the evidence becomes strong.
Every city is scored on both measures each month, ordered best to worst, and tracked over time. On the August 2026 reading, Zaragoza leads on 15-Minute Reach at 10.40 km, ahead of Utrecht at 10.05 km and Apeldoorn at 9.96 km; Berlin sits last at 6.15 km, with Kraków at 6.25 km and London at 6.77 km just above it. On idling, Zaragoza is again the lowest at 6.3%, followed by Rome at 6.5% and Apeldoorn at 6.6%, while Milan is the highest at 17.7%, with Munich at 14.4% and Paris and Berlin both at 14.3%.
Averaged across 2026 as a whole, the launch findings placed Zaragoza, Apeldoorn and Utrecht highest on reach and Berlin, Paris and Kraków lowest — a different ordering from any single month’s, which is why the monitor publishes the monthly table alongside the series behind it.
Three Findings
1. Congestion comes in two structural shapes
Looking at where idling occurs, rather than only how much of it there is, separates two entirely different urban conditions.
In Milan, inbound approach idling runs at 12.9%, more than double the 32-city median and higher than the within-city idling of most cities in the panel. Inside the core it reaches 24.4%. Paris shows an almost identical structure. Congestion in these metropolitan areas is a regional condition: it begins well outside the city and intensifies within it.
Zaragoza and Valladolid invert the pattern. Their approach roads are among the freest-flowing in the study, at 2.4% and 4.0% inbound idling, yet within-city idling rises to 9.6% and 13.3% – jumps of 4.0x and 3.3x on crossing the urban perimeter. Łódź shows the same shape.
Read only the headline numbers and congestion looks like a big-city problem. Read the structure and the conclusion changes: what separates these cities is not how much delay they impose but where the delay sits. A ring-road intervention would do nothing for Zaragoza; kerbside and loading reform would barely touch Milan’s approach corridors.
2. Munich is measurably getting worse for last-mile vans
Comparing each 2026 month against the same month a year earlier, Munich’s idling intensity is up in all eight comparable months, by an average of 17%, while 15-minute reach is down in all eight, averaging −4.3%. Two independently computed metrics moving in the same direction every single month is a consistent enough pattern to read as genuine, gradual deterioration rather than noise – and it is a claim that can only be made because the basis of measurement did not move between those months.
The caveat matters as much as the headline. The trend is driven overwhelmingly by delivery vans, whose idling is up 32% year on year, while trucks barely move at 2.2%. The precise statement is that last-mile van traffic in Munich is getting worse, not that Munich traffic in general is.
3. Poland is the only market improving across the board
Every Polish city in the panel shows more distance covered per fifteen minutes and flat or lower idling, in nearly every one of the eight comparable months from January to August. No other country in the study shows that consistency across all of its cities – five cities, two independently computed metrics, eight months, one direction. It is the kind of finding that only becomes visible once a stable series exists to look along.
From Benchmark to Operational Intelligence
The two published metrics are a starting point rather than the destination. The value of a stable measurement system is that it gives every subsequent question a baseline to be measured against.
The same foundation supports targeted studies of commercial traffic: how congestion and idling behave around major events, roadworks, extreme weather and seasonal delivery peaks. The value in each case comes from the comparison – disruption assessed against a normal operating baseline that was itself measured consistently, rather than against a generic average or a recollection of how things usually are. That is what lets an operator quantify the cost of a disruption after the fact, and a city or an operator anticipate pressure points before they arrive.
Connected Vehicle Data as Measurement Infrastructure
The Commercial Trip Monitor sits alongside other work in which connected vehicle data is used to measure the physical network directly rather than model it indirectly – from identifying where electric HGV charging infrastructure should be built, to quantifying road condition and corridor-level demand.
The broader opportunity goes beyond congestion benchmarking. Aggregate real vehicle behaviour at sufficient scale, hold the measurement constant over time, and parts of the physical world that were previously understood through assumptions can be measured directly. The Commercial Trip Monitor is one example of how connected-vehicle data can become measurement infrastructure for fleets, transport networks and the systems built around them.
🔗 Explore the Commercial Trip Monitor
Why guess, when mobility data can guide you? Talk to Mobito’s team about applying connected vehicle data to your network.