Monitor Fuel Price Trends for Logistics Budgets
Track daily AAA fuel averages as structured JSON — spot week-over-week diesel moves early and keep logistics budgets honest.
TL;DR — The Fuel Prices Scraper returns AAA's daily fuel averages — current plus yesterday/week/month/year-ago — as JSON. Schedule it, and a diesel surge hits your dashboard the morning it happens, not at month's end.
Why fuel trends surprise logistics budgets
Diesel moves fast when it moves. Fuel sits near a quarter of trucking's marginal cost per ATRI's operational-cost research — a ten-cent weekly swing across a national fleet is a budget line, not a rounding error.
The job: a daily dated feed of averages with built-in comparisons, so variance conversations start from numbers instead of screenshots.
How does this compare to the alternatives?
| Manual AAA checks | EIA weekly reports | Thirdwatch actor | |
|---|---|---|---|
| Cost | Free, sporadic | Free, weekly lag | Pay per result |
| Reliability | Habit-dependent | Authoritative, slower | Daily, structured |
| Setup time | Zero | Report reading | Minutes |
| Maintenance | Memory | Mondays only | Scheduled runs |
EIA data is the gold standard for analysis — but weekly. AAA's daily averages catch the move while it's moving.
How to monitor trends in 4 steps
How do I get the daily feed?
import os
from apify_client import ApifyClient
client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("thirdwatch/fuel-prices-scraper").call(
run_input={"states": []} # empty = national table only
)
rows = list(client.dataset(run["defaultDatasetId"]).iterate_items())Add state names to states for the corridors you operate.
How do I spot the moves?
import pandas as pd
df = pd.DataFrame(rows)
df["wow"] = df["diesel"] - df["week_ago"]
alerts = df[df.wow.abs() > 0.10][["region", "diesel", "week_ago", "wow"]]
print(alerts)Ten cents week-over-week is a reasonable alert band for diesel — tune to your exposure.
How do I store the series?
df.to_json("fuel_trend.jsonl", orient="records", lines=True, mode="a")A JSONL append per run builds the history — though each row already carries week_ago/month_ago/year_ago for instant context.
How do I feed the surcharge table?
Read diesel per region into your surcharge formula weekly — the dated record is the auditable basis for what customers get billed.
Sample output
{"region": "National average", "scope": "national", "date": "2026-09-21",
"regular": 3.42, "mid_grade": 3.81, "premium": 4.12,
"diesel": 3.98, "e85": 2.85,
"yesterday": 3.41, "week_ago": 3.36, "month_ago": 3.50, "year_ago": 3.31}Common pitfalls
Averages smooth regional variance — corridors can run hotter than their state. Daily fixes lag intraday spikes; for surge pricing contracts, pair with the weekly EIA reference. Nulls mean AAA didn't publish that grade for that region. The actor keeps the feed honest; thresholds and hedging are your policy.
Related use cases
Frequently asked questions
How often do the averages change?
AAA publishes fresh averages daily. A scheduled morning run captures the new fix; the week_ago and month_ago columns in each record show trend at a glance.
Can I alert on big diesel moves?
Yes — compare diesel to week_ago per record and alert past your threshold. The comparison fields ship inside the row, so alerting is arithmetic.
Does it cover national and state levels?
Every run returns the national table plus per-state rows for the states you list — scope marks which is which.
What grades are tracked?
Regular, mid-grade, premium, diesel, and E85, each with current and historical-comparison columns where AAA publishes them.
Related
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