Why vehicle vocation insight is essential for grant narratives

In a funding landscape defined by oversubscribed programs like RAISE and INFRA, ‘black box’’ data no longer suffices. Federal reviewers and other stakeholders increasingly scrutinize the data integrity behind the proposal to understand how the metrics were derived and whether or not they are believable.

Strong plans offer clear answers to fundamental questions: What kinds of trucks are using this corridor? Why are they there? What’s driving congestion, safety issues or infrastructure strain? And what verifiable data proves it?

Many agencies don’t have that level of insight because standard tools only count vehicles, not their intent, and black box algorithms are difficult to understand. Altitude’s Vocation framework solves both of those problems.

Identifying what trucks are doing

Traditional counters can’t distinguish a local service van from a long-haul freight truck. They can’t show whether a peak-hour spike is caused by last-mile parcel vans, recurring regional delivery loops or interstate freight. Yet these distinctions directly affect project evaluation.

Altitude’s Vehicle Vocation model fills this gap by classifying vehicles into five categories that are aligned with industry terminology and regulations:

• Local – within 150 air-miles, aligned with Federal Motor Carrier Safety Administration (FMCSA) short-haul rules. This data is critical for electrifying fleets and last-mile emissions modeling
• Regional-haul – moderate range, frequent return to domicile. These metrics distinguish short-term staging needs from overnight parking
• Long-haul – large geographic footprint, low domicile return rates. The classification helps in sizing overnight parking capacity on interstate corridors
• Hub-and-spoke – multiple daily loops from a central base. This category separates through-traffic from economic activity centers
• Door-to-door / Last-mile – high stop frequency, short duration. These insights help solve curbside double-parking and safety conflicts.

By using unsupervised machine learning to analyze behavior across five million vehicles, Altitude removes human bias and reveals the natural operational clusters of freight movement. This provides planners with mathematical ‘ground truth’ rather than arbitrary definitions.

Figure 1: Altitude’s Vehicle Vocation classifications sort freight traffic into five funding-aligned categories.

With vocation intelligence, planners can finally explain why freight is moving the way it is, and where investment is needed to support economic growth.

Defensible freight evidence wins grants

Reviewers reward applications that present a clear problem and back it with auditable data. Vocation-based insight makes this possible through:

• Behavioral clarity: Agencies can demonstrate whether a corridor is primarily a long-haul freight spine, a regional distribution zone or a last-mile delivery hotspot.
  
• Regulatory alignment: The 150-mile Local definition matches FMCSA’s short-haul exemption, reducing ambiguity in grant narratives.
  
• Long-term trend integrity: Because our Gaussian Mixture Models (GMM) identify natural behavioral clusters rather than using manual cut-offs, classifications remain consistent over time. This ensures your trend lines reflect real economic shifts, not algorithm noise.

When reviewers see that actual vehicle behavior drives the analysis, a grant narrative becomes significantly more compelling. Especially when that data is clearly explainable to stakeholders in a town hall or federal review board. Altitude’s vocations are based on transparent behavioral thresholds (like range and stop frequency), not a black box.

Vocation intelligence sharpens project justification

Today’s funding environment pushes planners to defend their projects with a more nuanced understanding of freight movements. Vocation data helps agencies isolate and quantify the forces shaping corridor performance. For consultants, this means moving beyond generic heavy-vehicle counts to deliver precise corridor studies that differentiate the analysis.

Here are three high-impact use cases for strengthening applications:

Identify the true source of congestion or delay

Instead of saying “traffic is heavy,” planners can show whether the bottleneck is caused by:

• Last-mile delivery vehicles making dense micro-stops
• Hub-and-spoke loops returning repeatedly to a distribution center
• Long-haul freight creating heavy early-morning flows

Each scenario leads to a different, better-targeted solution and a stronger justification for investment.

Demonstrate economic activity linked to infrastructure needs

Vocation data reveals the freight purpose driving trips. Reviewers can better see whether the corridor supports:

• Regional freight distribution
• Local commercial delivery
• Cross-state long-haul freight

This helps agencies tie project benefits to economic outcomes, which grant reviewers prioritize.

Build more precise cost-benefit analyses

When vocation is combined with weight class and industry, planners can quantify:

• The infrastructure burden of heavy-duty long-haul trucks
• The safety impacts of stop-intensive last-mile operations
• The emissions implications of local vs. regional freight

This granularity creates stronger cost-benefit ratios and clearer operational narratives.

The bottom line: Detailed freight assumptions win grants

Grant reviewers don’t just want to know that trucks use a specific corridor. They want to know which trucks are doing what and how often, and why it justifies investment.

As funding tightens, agencies with access to defensible, behavior-based freight data will outperform those relying on counts and assumptions. Altitude’s Vehicle Vocation classifications give planners the ability to quantify freight purpose with clarity and confidence.

Download the white paper to learn more about freight planning methodology with defensible, behavior-based vocation data that strengthens your grant narratives.