🚚 DAT Freight Rates Scraper

🚀 Pull live US and Canadian freight market rates in seconds. Filter by equipment, rate type, country, and time bucket. No API key, no registration, no manual CSV wrangling.

🕒 Last updated: 2026-05-16 · 📊 24 fields per record · 4 datasets in one feed · 183 state-level supply/demand rows · 3 years of monthly rate history

DAT Freight & Analytics runs the largest load board in North America, and the Trendlines pages it publishes are the closest thing the trucking industry has to a public price index. This scraper turns those pages into a flat Apify dataset. Every run captures the national rate history (spot and contract, weekly and monthly, going back roughly three years), the load-to-truck ratio for every US state and Canadian province, the week, month, and year-over-year percentage moves, and the current national diesel reference price. The output is one row per data point, which is the shape spreadsheets, Postgres, and pandas already speak.

Freight brokers, dispatchers, and 3PL pricing teams check DAT daily to figure out what a lane should pay. When the dashboard hangs or the iQ subscription is between renewals, the only fallback is calling carriers one by one. This actor gives you a programmatic version of the public Trendlines view, so quotes go out backed by current numbers and contract negotiations cite the same baseline both sides reference. Pair it with a scheduled Apify run and you have a daily rate sheet feeding your CRM, BI tool, or rate sheet, with zero scraping plumbing to maintain.

🎯 Target audience	💡 Primary use cases
Freight brokers and dispatcher teams	Daily lane-context rate checks for shipper quotes
Owner-operator carriers	Verify carrier pay against published spot rates
3PL pricing and procurement teams	Build a market-truth baseline for RFP responses
Logistics software vendors	Power rate widgets without paying a market data subscription
Supply chain analysts and researchers	Track freight cycle indicators alongside fuel and demand
Shippers verifying invoices	Spot underbid lanes and overcharged contracts

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📋 What the DAT Freight Rates Scraper does

• 📡 National rate history. Monthly buckets back ~3 years and weekly buckets back ~1 year, with the average rate per mile in USD plus the average fuel surcharge per trip.
• 🚚 Load-to-truck ratio per state. One row per US state and Canadian province for van, flatbed, and reefer, with the load count, truck count, and computed ratio.
• 📈 Trend percentages. Week, month, and year-over-year change for spot rate, load-to-truck ratio, load postings, truck postings, and fuel.
• ⛽ National diesel price. The reference price per gallon DAT publishes alongside the rates.
• 🎛️ Smart filters. Restrict by equipment (van, flatbed, reefer, intermodal), rate type (spot shipper-to-broker, spot broker-to-carrier, contract), country (US, CA), period type (month, week), or state code.
• 🧾 One flat row per data point. Every dataset writes to the same shape, so SQL joins and pandas pivots are trivial.

Every record carries its source URL on dat.com, the dataset tag, every relevant filter (equipment, rate type, country, state, period), the numeric values, and a precise scrape timestamp. The schema is stable, so daily appends stitch into a clean longitudinal store.

💡 Why it matters: A broker who avoids one underquoted load per week saves hundreds of dollars in margin. Owning a current rate feed instead of refreshing a vendor dashboard ten times an hour pays for itself within the first month.

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🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough of the input form, a sample run, and a Google Sheets dashboard that consumes the dataset.

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⚙️ Input

Field	Type	Description	Default
maxItems	integer	Cap on the number of records returned. Free users are limited to 10.	10
datasets	array of enum	Which Trendlines datasets to scrape: national_history, load_truck_ratio, national_trends, fuel_price.	All four
equipments	array of enum	Equipment categories to include: VAN, FLATBED, REEFER, INTERMODAL.	["VAN", "FLATBED", "REEFER"]
rateTypes	array of enum	Rate types for national_history: SHIPPER_TO_BROKER_SPOT, BROKER_TO_CARRIER_SPOT, CONTRACT.	All three
countries	array of enum	Countries for national_history: US, CA.	["US", "CA"]
periodTypes	array of enum	Time bucket granularity for national_history: Month (~36 buckets) or Week (~52 buckets).	["Month", "Week"]
states	array of enum	Optional list of state or province ISO codes to filter the load_truck_ratio dataset.	All states

Example: van and reefer monthly US spot rates plus load-to-truck for Texas and California.

{
    "datasets": ["national_history", "load_truck_ratio"],
    "equipments": ["VAN", "REEFER"],
    "rateTypes": ["SHIPPER_TO_BROKER_SPOT"],
    "countries": ["US"],
    "periodTypes": ["Month"],
    "states": ["TX", "CA"]
}

Example: full national snapshot for a daily warehousing job.

{
    "datasets": ["national_history", "load_truck_ratio", "national_trends", "fuel_price"],
    "equipments": ["VAN", "FLATBED", "REEFER"],
    "rateTypes": ["SHIPPER_TO_BROKER_SPOT", "BROKER_TO_CARRIER_SPOT", "CONTRACT"],
    "countries": ["US", "CA"],
    "periodTypes": ["Month", "Week"]
}

⚠️ Good to Know: This actor pulls from the public Trendlines API that powers dat.com/trendlines. The numbers are the same ones DAT publishes to the general public. Lane-level rates (specific origin-destination city pairs) are gated behind DAT's paid iQ subscription and are not part of this scraper's scope.

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📊 Output

Every record is one flat object representing a single data point from one of the four datasets.

🧾 Schema

Field	Type	Example
🆔 recordId	string	national_history|VAN|SHIPPER_TO_BROKER_SPOT|US|Month|2026-04-01
🏷️ dataset	string	national_history
🚛 equipment	string	VAN
💸 rateType	string	SHIPPER_TO_BROKER_SPOT
🌎 country	string	US
📍 stateCode	string	TX
⏲️ periodType	string	Month
📅 periodStart	string	2026-04-01
🗓️ year	integer	2026
🔢 month	integer	4
💰 rateUsd	number	2.24
⛽ fuelSurchargePerTripUsd	number	0.71
📦 loadCount	number	11842.5
🚚 truckCount	number	743.1
⚖️ loadToTruckRatio	number	9.915
🛢️ fuelPricePerGallonUsd	number	5.64
📊 metric	string	spot_rate
📈 weekOverWeekPct	number	1.322
📉 monthOverMonthPct	number	0
🗓️ yearOverYearPct	number	4.455
🕒 asOfDate	string	2026-05-11
🔗 sourceUrl	string	https://www.dat.com/trendlines/van/national-rates
⏰ scrapedAt	string	2026-05-16T04:56:50.643Z
❗ error	string	(populated only on failure)

📦 Sample records

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✨ Why choose this Actor

Capability	What you get
🧾	Four datasets in one feed. National rate history, state-level supply/demand, trend percentages, and fuel price land in the same dataset.
🚛	Every equipment category that matters. Van, flatbed, reefer, and intermodal in one input, no separate runs per truck type.
⏳	Three years of rate history. Monthly buckets back ~36 periods and weekly buckets back ~52, ready for time-series modelling.
🌎	US and Canadian coverage. State and provincial rows on the same schema, so cross-border lanes share a baseline.
🎛️	Filters at every axis. Restrict equipment, rate type, country, period, state, or dataset without writing code.
🚦	Built-in retries. Handles 429 and 503 from the upstream gracefully so scheduled jobs survive load spikes.
🚀	No API key. Public DAT Trendlines endpoints only, so you ship a market data feed without keys or contracts.

📊 A full run with default filters produces roughly 1,800 records covering three years of national rate history, current state-level supply/demand for three equipment categories, every published trend percentage, and the latest diesel reference price.

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📈 How it compares to alternatives

Approach	Cost	Coverage	Refresh	Filters	Setup
⭐ DAT Freight Rates Scraper (this Actor)	Pay-per-run	4 datasets, US and CA	On demand or scheduled	Equipment, rate type, country, period, state	Click run
Paid live freight market APIs	High monthly subscription	Multi-source	Streaming	Limited	API key, contract
Official trade publications	Free	Headlines and weekly summaries	Weekly	None	Read and copy
Legacy community spreadsheets	Free	Stale or partial	Quarterly at best	None	Manual joins
Build your own scraper	Engineering hours	Whatever you build	Whatever you build	Whatever you build	Significant engineering

If you want a turnkey freight market data feed without paying a paid index subscription or maintaining your own crawler, this actor is the shortest path.

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🚀 How to use

1. 📝 Sign up for Apify. Create a free account at console.apify.com/sign-up?fpr=vmoqkp.
2. 🎛️ Pick your datasets. Leave the defaults to grab everything, or narrow to just national_history for a pure time-series pull.
3. 🔍 Set your filters. Choose equipment, rate type, country, period granularity, and optional state codes.
4. ▶️ Run. Click Start and watch the dataset fill.
5. 📤 Export. Pull JSON, CSV, or XLSX, or wire the dataset into Make, Zapier, BigQuery, Postgres, or your own webhook.

⏱️ Total time: Under two minutes from sign-up to first dataset row.

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💼 Business use cases

🚚 Freight brokers Quote shippers with current spot rate context Avoid underquoting on van, flatbed, and reefer lanes Audit invoices against published contract rates Spot supply-tight states to repost loads at higher pay	🛻 Owner-operators and small carriers Negotiate truck rate floors with confidence Reposition empty trucks toward high load-to-truck states Track fuel surcharge baselines for billing Validate broker-quoted pay against the published spot
📊 3PL pricing and procurement teams Build RFP responses on a documented market baseline Compare carrier bids against contract reference rates Forecast budget impact when fuel surcharges shift Track year-over-year freight inflation by equipment	🧑‍💻 Logistics software vendors Power a rate sheet feature without buying a paid feed Embed market indicators inside TMS and ERP dashboards Trigger pricing alerts on threshold breaches Differentiate analytics products with built-in baselines

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🌟 Beyond business use cases

Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.

🎓 Research and academia Empirical datasets for papers, thesis work, and coursework Longitudinal studies tracking changes across snapshots Reproducible research with cited, versioned data pulls Classroom exercises on data analysis and ethical scraping	🎨 Personal and creative Side projects, portfolio demos, and indie app launches Data visualizations, dashboards, and infographics Content research for bloggers, YouTubers, and podcasters Hobbyist collections and personal trackers
🤝 Non-profit and civic Transparency reporting and accountability projects Advocacy campaigns backed by public-interest data Community-run databases for local issues Investigative journalism on public records	🧪 Experimentation Prototype AI and machine-learning pipelines with real data Validate product-market hypotheses before engineering spend Train small domain-specific models on niche corpora Test dashboard concepts with live input

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🔌 Automating DAT Freight Rates Scraper

Wire this actor into the rest of your stack with a few lines of code or a saved schedule.

• 🟢 Node.js client for calling the actor from JavaScript or TypeScript projects.
• 🐍 Python client for notebooks, FastAPI endpoints, or Airflow DAGs.
• 📚 Apify API reference for raw REST integration in any language.

Schedules turn this actor into a low-cost market data subscription. Run it daily for an always-fresh rate sheet feeding your CRM, hourly during contract season for rapid bid reactions, or weekly for a longitudinal warehouse you can backfill into BigQuery or Postgres. The dataset chains naturally with downstream actors and webhooks, so you can branch on a load-to-truck ratio threshold or post a Slack alert when the national diesel price moves more than a configured percentage.

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❓ Frequently Asked Questions

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🔌 Integrate with any app

• Zapier - route every new dataset row to spreadsheets, Slack, or your CRM.
• Make - chain Apify runs into visual automations with conditional logic.
• n8n - self-hosted workflows for full data ownership.
• Google Sheets - drop CSV exports into shared sheets for rate-desk review.
• Airtable - structured base for cross-equipment rate trackers.
• Slack - push spread alerts and tight-supply notifications straight into channels.

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🔗 Recommended Actors

• 📰 Freightwaves Scraper - freight news, analytics, and SONAR commentary alongside this rate feed.
• 🛡️ FMCSA Carrier Safety Scraper - safety ratings and inspection history for carriers you are about to dispatch.
• 📦 UPS Package Tracking Scraper - parcel-side visibility to complement truckload rates.
• 🚚 FedEx Package Tracking Scraper - second parcel network for end-to-end shipment dashboards.
• 📈 FRED Economic Data Scraper - macro indicators (fuel CPI, freight indices, GDP) to contextualise the freight cycle.

💡 Pro Tip: browse the complete ParseForge collection for more logistics, freight, and market-data actors.

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🆘 Need Help? Open our contact form and we will respond within one business day.

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⚠️ Disclaimer: This actor extracts publicly available freight market data from the DAT Trendlines pages for informational purposes only. None of the data returned constitutes pricing or financial advice. You are responsible for ensuring your use complies with DAT's terms of service and any obligations that apply in your jurisdiction, especially when redistributing data.