Has it happened to you? A planner submits a grant application citing heavy truck volume as a reason to build more overnight truck parking, not knowing that most of those trucks are actually local units that go home every night. The application gets funded, the wrong infrastructure gets built, and the real problem remains unsolved.
Vehicle counts and weight class data may feel like proof for plans, but without vehicle vocation context they can mislead the entire narrative. For example:
Curbside management gap: A city applies for loading zone improvements based on general commercial vehicle counts. Without identifying door-to-door vehicles specifically, the application can’t quantify the double-parking problem or justify the scope of the investment to reviewers.
A heavy-duty truck doing 10 local loops a day and a long-haul carrier crossing three states are the same vehicle class, but they have entirely different infrastructure demands. Vocation classification answers the question reviewers increasingly ask: not just how many trucks, but what are they doing and why are they here?
Altitude by Geotab classifies commercial vehicles into five vocations:
Figure 1: The vehicle vocation system clusters every active vehicle into one of five vocations on a monthly basis based on activity in the previous three months. The system first analyzes raw telematics data (such as location, time, and speed) to create Contextualized Vehicle Movement Datasets of a vehicle’s domicile, the stops it makes, and its duty cycles.
Combining vocation with vehicle weight class adds further precision. Separating medium-duty delivery vans from heavy-duty regional freight trucks gives reviewers the specificity they need to evaluate whether the proposed investment matches the documented demand.
For grant writers, the most compelling data is data that already speaks the language of the funding program. Altitude’s local vocation threshold of under 150 air-miles aligns directly with the Federal Motor Carrier Safety Administration (FMCSA) Hours of Service short-haul exemption, meaning that planners aren’t introducing a new definition they have to defend, they’re reinforcing one reviewers already recognize.
This classification formatting extends to regional-haul and long-haul. Definitions that are built around domicile habits and miles traveled mirror how FMCSA and the broader industry already draw these distinctions. Regulatory alignment removes confusion in grant review and enhances credibility.
A grant application with vocation data can be compellingly specific. Rather than writing “significant delivery activity was observed on this corridor,” a planner can write: “Vehicles averaging more than 27 stops per day with stop durations under 108 seconds were identified, consistent with parcel delivery and waste collection operations — indicating concentrated curbside demand that current loading zone infrastructure cannot accommodate.”
That specificity matters because it gives reviewers something to evaluate rather than accept on faith. Contrast this with a generic commercial vehicle count and anecdotal traffic observations, which force reviewers to make assumptions about what the traffic actually means. Auditable thresholds grounded in natural data clusters — not arbitrary human-drawn cutoffs — hold up to scrutiny in ways that raw counts simply don’t.
Competitive grant applications don’t just describe a problem, they prove it. Vehicle class data describes the traffic and vocation data explains it. When your narrative states exactly what type of freight activity is occurring, ties it to a recognized regulatory definition and backs that up with measurable behavioral thresholds, you’ve moved from observation to evidence. That’s the difference between an application that merely sounds credible and one that actually is.
Download our Vocations white paper to learn more about freight planning methodology with defensible, behavior-based vocation data that strengthens your grant narratives.
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