North America weekly rail freight intelligence

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Weekly rail traffic panel for the United States, Canada, Mexico, and North America (AAR reporting weeks 26-38 of 2026, weeks ending 2026-07-04 .. 2026-09-26), extracted from the Association of American Railroads' public weekly press releases. Each (week_end, region) row carries observed carloads, intermodal units, totals and year-over-year percent changes, year-to-date cumulative volumes, plus derived demand intelligence: intermodal share (a consumer-goods flow proxy), trailing 4-week y/y momentum, a fixed-cut demand regime (expanding/steady/soft/contracting), and a 0-100 freight pulse score. US rows add commodity-group breadth (share of 10 groups up y/y) and the top gaining/declining commodity group. Units are railcars / containers-and-trailers per week; y/y changes are percent. Caveat: weekly traffic is noisy (holiday weeks, weather); prefer momentum_4w_pct and demand_regime over single-week spikes. A coincident freight-demand signal, not a forecast.

Source: AAR Weekly Rail Freight Intelligence
Rows: 52
Columns: 29
Source cadence: Weekly
Last refreshed: Oct 1, 2026
Theme: economy

Column	Type	Description
week_end	string	ISO date of the Saturday ending the AAR reporting week.
week_number	integer	AAR reporting week number within the calendar year.
region	string	Region label: United States, Canada, Mexico, North America.
country_code	string	ISO alpha-3 country code (USA/CAN/MEX); NAM is the curator aggregate code for the AAR North America total.
carloads	integer	Carloads originated in the week (railcars).
carloads_yoy_pct	float	Year-over-year percent change of carloads vs the same week last year. (unit: percent)
intermodal_units	integer	Intermodal containers and trailers originated in the week.
intermodal_yoy_pct	float	Year-over-year percent change of intermodal units. (unit: percent)
total_units	integer	Carloads + intermodal units in the week.
total_yoy_pct	float	Year-over-year percent change of total units. Release-quoted for the US and North America; reconstructed for Canada and Mexico from the quoted carload/intermodal y/y rates (see module docstring) — those rows are flagged by total_yoy_derived. (unit: percent)
total_yoy_derived	boolean	True when total_yoy_pct was reconstructed from the carload/intermodal y/y rates (Canada, Mexico); False when quoted directly in the release (US, North America).
intermodal_share_pct	float	100 * intermodal_units / total_units — consumer-goods flow proxy. (unit: percent)
ytd_total_units	integer	Year-to-date cumulative carloads + intermodal units.
ytd_total_yoy_pct	float	Year-over-year percent change of the YTD cumulative total. (unit: percent)
ytd_carloads	float	Year-to-date cumulative carloads (US only; null elsewhere).
ytd_carloads_yoy_pct	float	Y/y change of YTD carloads (US only). (unit: percent)
ytd_intermodal_units	float	Year-to-date cumulative intermodal units (US only).
ytd_intermodal_yoy_pct	float	Y/y change of YTD intermodal units (US only). (unit: percent)
n_commodity_groups_up	float	Number of the 10 AAR carload commodity groups up y/y that week (US only).
top_gaining_group	string	Commodity group with the largest absolute carload gain that week (US only).
top_gaining_group_wow_change	float	Absolute carload change of the top gaining group (US only).
top_declining_group	string	Commodity group with the largest absolute carload decline that week (US only).
top_declining_group_wow_change	float	Absolute carload change of the top declining group, negative (US only).
momentum_4w_pct	float	Mean of total_yoy_pct over the trailing 4 weeks (null for the first 3 weeks). (unit: percent)
demand_regime	string	Fixed-cut regime from total_yoy_pct: expanding (>=3), steady (>= -1), soft (>= -4), contracting (< -4).
freight_pulse_score	integer	0-100 per-region demand-strength gauge: 50 + 12 * z(total_yoy_pct) over the window, clipped.
source_url	string	AAR press-release URL the week's figures were extracted from.
collected_at	string	Date the release was collected (ISO).
row_hash	string	md5 over week_end\|region\|total_units\|total_yoy_pct\|freight_pulse_score.

First 10 sample rows — a preview, not the complete dataset.

week_end	week_number	region	country_code	carloads	carloads_yoy_pct	intermodal_units	intermodal_yoy_pct	total_units	total_yoy_pct	total_yoy_derived	intermodal_share_pct	ytd_total_units	ytd_total_yoy_pct	ytd_carloads	ytd_carloads_yoy_pct	ytd_intermodal_units	ytd_intermodal_yoy_pct	n_commodity_groups_up	top_gaining_group	top_gaining_group_wow_change	top_declining_group	top_declining_group_wow_change	momentum_4w_pct	demand_regime	freight_pulse_score	source_url	collected_at	row_hash
2026-07-04	26	Canada	CAN	88,601	8.1	70,562	-4.1	159,163	2.33	true	44.33	4,277,145	0.6	—	—	—	—	—	—	—	—	—	—	steady	47	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-4-2026/	2026-10-01	74c2d30c554939c1af5b53d0cce7bcb8
2026-07-04	26	Mexico	MEX	15,445	42.2	14,611	56	30,056	48.59	true	48.61	673,753	9.2	—	—	—	—	—	—	—	—	—	—	expanding	70	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-4-2026/	2026-10-01	c368c9a6489c1d06c18a9448b12a21ae
2026-07-04	26	North America	NAM	316,737	6.3	354,603	10.3	671,340	8.4	false	52.82	18,099,612	2.9	—	—	—	—	—	—	—	—	—	—	expanding	64	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-4-2026/	2026-10-01	8e4d9fd29a49ac6a2f19d6d149efcc5f
2026-07-04	26	United States	USA	212,691	3.7	269,430	12.9	482,121	8.7	false	55.88	13,148,714	3.4	5,894,302	3.2	7,254,412	3.6	9	grain	2,490	coal	-4,197	—	expanding	65	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-4-2026/	2026-10-01	e7158cec2a48cdaf9ee89b8e28f13cf5
2026-07-11	27	Canada	CAN	89,784	1.9	73,506	-1.4	163,290	0.39	true	45.02	4,440,435	0.6	—	—	—	—	—	—	—	—	—	—	steady	41	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-11-2026/	2026-10-01	2cc97528546ecd5f69549a4430ed6087
2026-07-11	27	Mexico	MEX	14,182	24.6	14,082	55.4	28,264	38.25	true	49.82	700,187	9.8	—	—	—	—	—	—	—	—	—	—	expanding	63	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-11-2026/	2026-10-01	c88550cfdf6bd73a1e4e221731eb49af
2026-07-11	27	North America	NAM	327,006	1.1	368,073	3.4	695,079	2.3	false	52.95	18,792,861	2.9	—	—	—	—	—	—	—	—	—	—	steady	43	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-11-2026/	2026-10-01	caa020e6088463aaca565a2bc6ac5e83
2026-07-11	27	United States	USA	223,040	-0.4	280,485	3	503,525	1.5	false	55.7	13,652,239	3.4	6,117,342	3.1	7,534,897	3.6	8	metallic ores and metals	1,627	coal	-4,713	—	steady	42	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-11-2026/	2026-10-01	61b42ef525b81661badf3545d1d30474
2026-07-18	28	Canada	CAN	88,318	3.8	71,076	-3.2	159,394	0.56	true	44.59	4,599,829	0.6	—	—	—	—	—	—	—	—	—	—	steady	42	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-18-2026/	2026-10-01	8f9c31c941fb1f94d435ef18c363bb4e
2026-07-18	28	Mexico	MEX	13,058	12.2	14,408	53	27,466	30.45	true	52.46	727,653	10.5	—	—	—	—	—	—	—	—	—	—	expanding	58	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-july-18-2026/	2026-10-01	5d330f7c2014a3a6b67ce4ed8d35d07f

Profiled Oct 1, 2026 from snapshot 20261001T170411Z-4548a7ee3a55

Measured

Completeness: 75.9%
Rows: 52
Columns: 29
Columns with gaps: 10

Column	Missing	Distinct	Range	Distribution
week_endvarchar	0%	15	—	2026-07-044 2026-07-114 2026-07-184
week_numberbigint	0%	15	26 → 38median 32
regionvarchar	0%	4	—	Canada13 Mexico13 North America13
country_codevarchar	0%	4	—	CAN13 MEX13 NAM13
carloadsbigint	0%	56	11,710 → 346,191median 153,811	2 outside 1st–99th percentile
carloads_yoy_pctdouble	0%	58	-16.8 → 42.2median 3.05	2 outside 1st–99th percentile
intermodal_unitsbigint	0%	54	12,317 → 395,417median 173,393	2 outside 1st–99th percentile
intermodal_yoy_pctdouble	0%	49	-8.3 → 64.5median 5.45	2 outside 1st–99th percentile
total_unitsbigint	0%	49	24,027 → 741,608median 326,484	2 outside 1st–99th percentile
total_yoy_pctdouble	0%	47	-7.39 → 48.59median 3.75	2 outside 1st–99th percentile
total_yoy_derivedboolean	0%	2	—	false26 true26
intermodal_share_pctdouble	0%	55	42 → 56.69median 52.74	2 outside 1st–99th percentile
ytd_total_unitsbigint	0%	43	673,753 → 26,678,211median 9,700,534	2 outside 1st–99th percentile
ytd_total_yoy_pctdouble	0%	27	0.5 → 11.5median 3.3	1 outside 1st–99th percentile
ytd_carloadsdouble	75%	12	5,894,302 → 8,679,882median 7,276,025	2 outside 1st–99th percentile
ytd_carloads_yoy_pctdouble	75%	5	2.7 → 3.2median 2.7	1 outside 1st–99th percentile
ytd_intermodal_unitsdouble	75%	14	7,254,412 → 10,778,696median 9,005,409	2 outside 1st–99th percentile
ytd_intermodal_yoy_pctdouble	75%	7	3.6 → 4.2median 3.8	1 outside 1st–99th percentile
n_commodity_groups_updouble	75%	6	3 → 9median 7	1 outside 1st–99th percentile
top_gaining_groupvarchar	75%	3	—	metallic ores and metals9 grain3 chemicals1
top_gaining_group_wow_changedouble	75%	12	1,627 → 4,859median 2,261	2 outside 1st–99th percentile
top_declining_groupvarchar	75%	4	—	coal9 motor vehicles and parts2 chemicals1
top_declining_group_wow_changedouble	75%	13	-5,894 → -1,031median -3,547	2 outside 1st–99th percentile
momentum_4w_pctdouble	23.1%	44	0.62 → 40.59median 4.27	2 outside 1st–99th percentile
demand_regimevarchar	0%	4	—	expanding32 steady14 soft4
freight_pulse_scorebigint	0%	35	23 → 80median 48	2 outside 1st–99th percentile
source_urlvarchar	0%	11	—	https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-august-1-2026/4 https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-august-15-2026/4 https://www.aar.org/news/aar-reports-weekly-rail-traffic-for-the-week-ending-august-22-2026/4
collected_atvarchar	0%	1	—	2026-10-0152
row_hashvarchar	0%	55	—	041eed0f0ef402b053f663c436544aca1 067c292a60ac78c0f77b2a3daab283cb1 082137c586f73a05a7b7131b639748d31

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/aar_rail_freight_intel/na_weekly_rail_freight_weekly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/aar_rail_freight_intel/na_weekly_rail_freight_weekly").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/aar_rail_freight_intel/na_weekly_rail_freight_weekly

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261001T170411Z-4548a7ee3a55 and its content hash, so readers get exactly the data you used.

APA

AAR Weekly Rail Freight Intelligence. (2026). North America weekly rail freight intelligence [Data set, snapshot 20261001T170411Z-4548a7ee3a55, sha256 4548a7ee3a55]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/aar_rail_freight_intel/na_weekly_rail_freight_weekly?snapshot=20261001T170411Z-4548a7ee3a55

BibTeX

@misc{dz_aar_rail_freight_intel_na_weekly_rail_fr_4548a7ee,
  title        = {{North America weekly rail freight intelligence}},
  author       = {{AAR Weekly Rail Freight Intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/aar_rail_freight_intel/na_weekly_rail_freight_weekly?snapshot=20261001T170411Z-4548a7ee3a55}},
  note         = {Snapshot 20261001T170411Z-4548a7ee3a55, sha256 4548a7ee3a5594de4059241a0185f96d67101cbd52c0eaf26171e8ab4bc4d041; accessed 2026-10-02}
}

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North America weekly rail freight intelligence

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