In the span of 36 hours this week, two Union Pacific freight trains derailed on major corridors: one hauling hazmat through suburban Houston, the other scattering intermodal containers across the Southern California desert. RailState can now measure the current impact of these derailments on freight flows at both locations.
What Happened
On the morning of March 18, 23 cars of a manifest train carrying ethanol, LPG, and other hazardous materials derailed in Richmond, Texas, about 30 miles southwest of Houston. The next evening, roughly 20 cars of an intermodal train derailed in Mecca, California, on UP's Sunset Route, one of the railroad's primary transcontinental corridors, sending approximately 40 shipping containers tumbling across the desert floor. No injuries were reported at either site.
Both incidents shut down critical freight arteries. But measuring exactly how much traffic was disrupted, and how quickly, has traditionally been impossible without waiting weeks for railroad self-reported data. RailState's sensor network makes this data available in hours instead of weeks.
RailState operates a growing network of wayside sensors positioned along rail corridors across North America. These sensors automatically detect and classify every passing train, recording train type, individual car types, hazmat placards, container details, speed, and direction of travel. The result is a continuous, train-by-train, car-by-car record of freight movement at specific geographic points.
RailState Capturing Train Details During the Derailment
At Richmond, the RailState sensor sits outside of the rail right of way, just 0.6 miles from the derailment site. It was actively capturing data on this very train at the moment the cars left the rails.
The sensor began reading the westbound manifest train at 4:49 AM Central Time on March 18. The reading ended abruptly at 4:51 AM, just under two minutes in, when the derailment interrupted the train's passage. Before the disruption cut the reading short, RailState had already captured a detailed car-by-car inventory of the train's consist — a real-time manifest of exactly what was on the tracks when the incident occurred.
The analysis for this location splits March 18 at the approximate derailment time, comparing post-derailment traffic (March 18 after 4 AM CT through March 20) against the pre-disruption 7-day baseline (March 10-16).
The Derailed Train — Captured in Real Time
RailState's Richmond sensor was actively capturing this train at the exact time the derailment occurred. The sensor began reading the westbound manifest at March 18, 2026 at 4:49 AM Central Time and recorded the train traveling at 27.5 mph. The reading was cut short at 4:51 AM CT when the derailment interrupted the train's passage past the sensor. The derailment was reported to authorities at approximately 5:00 AM — minutes later.
Despite the interrupted reading, RailState captured detailed data on the majority of the consist, providing a car-by-car inventory of what was on the tracks at the moment of the incident: 3 locomotives and 85 freight cars, including 64 tank cars, 9 covered hoppers, 7 vehicular flatcars, and 5 box cars.
Train Consist Diagram
Each block represents one car, colored by type and hazmat placard. Scroll horizontally to see the full train.
Hazardous Materials Placards
Of the 64 tank cars captured, 54 displayed hazardous materials placards — a significant proportion of the train was carrying regulated commodities. The placards recorded by RailState's sensors detail exactly what was on the rails at the time of the incident:
| UN Placard | Commodity | Cars | Share of Hazmat |
|---|---|---|---|
| UN1075 | Liquefied Petroleum Gas (LPG) | 30 | 56% |
| UN1170 | Ethanol | 15 | 28% |
| UN1805 | Phosphoric Acid | 5 | 9% |
| UN1208 | Hexane | 2 | 4% |
| UN2078 | Toluene Diisocyanate | 1 | 2% |
| UN3475 | Ethanol/Gasoline Blend | 1 | 2% |
| Total Hazmat Cars | 54 | 100% | |
Measured Disruption — Severe
Comparing the post-disruption period (Post-4AM Mar 18) against the 7-day pre-disruption baseline, RailState sensors recorded steep declines across all metrics at Richmond, TX:
Traffic Trends — Last 30 Days
Red bars indicate post-disruption days. Green line is the 7-day moving average.
Volume by Category
| Car Type | Disruption Avg | Baseline Avg | Change |
|---|---|---|---|
| Box Car | 74.0 | 325.6 | -77.3% |
| Flat Car | 121.0 | 319.1 | -62.1% |
| Gondola | 170.3 | 479.0 | -64.4% |
| Hopper | 392.7 | 1802.0 | -78.2% |
| Intermodal | 55.0 | 416.9 | -86.8% |
| Other | 0.0 | 8.6 | -100.0% |
| Tank Car | 154.0 | 896.3 | -82.8% |
| Train Type | Disruption Avg | Baseline Avg | Change |
|---|---|---|---|
| Automotive | 0.3 | 1.9 | -82.1% |
| Coal Unit | 0.0 | 0.7 | -100.0% |
| Grain Unit | 0.7 | 6.3 | -89.4% |
| Intermodal | 0.7 | 4.0 | -83.3% |
| Manifest | 5.3 | 19.1 | -72.1% |
| Passenger | 0.0 | 0.6 | -100.0% |
| Petroleum Unit | 0.3 | 2.6 | -87.0% |
| Direction | Disruption Avg | Baseline Avg | Change |
|---|---|---|---|
| Eastbound | 2.7 | 15.7 | -83.0% |
| Westbound | 4.7 | 19.4 | -76.0% |
Derailment on UP's Major Intermodal Route from Southern California
At Mecca, the RailState sensor sits directly on the Sunset Route mainline, 7.3 miles from the derailment site. It had captured the full profile of the intermodal train the day before the incident, providing a complete record of the containers and cars involved.
Using 90 days of continuous baseline data, RailState quantified the disruption almost immediately. The impact was stark: eastbound container volume on the Sunset Route dropped sharply from baseline levels. That's a critical artery for intermodal freight moving from Southern California ports toward destinations across the Southwest, and the derailment choked it overnight.
This kind of granular, location-specific impact measurement, available within hours of an event, simply doesn't exist anywhere else. Traditional rail data sources rely on railroad self-reporting released with multi-week delays.
The Derailed Train — RailState Profile
RailState's sensor at Mecca captured the train that would later derail as it passed the sensor location on March 18, 2026 at 3:47 PM Pacific Time — approximately 26 hours before the derailment occurred roughly 7 miles to the east. The sensor recorded the following details:
Consist Breakdown
The train consisted of 4 locomotives and 66 well cars (185 platforms), carrying a total of 398 intermodal containers:
| Container Type | Count | Share |
|---|---|---|
| 40-foot Containers | 329 | 82.7% |
| 20-foot Containers | 63 | 15.8% |
| 45-foot Containers | 5 | 1.3% |
| 20-foot Tank Containers | 1 | 0.3% |
| Total | 398 | 100% |
The train was predominantly loaded with standard 40-foot international shipping containers (83% of the consist), consistent with an import-loaded intermodal service moving eastbound from the Southern California port complex. All containers were reported to contain non-hazardous products.
Eastbound Container Volume Impact
The Mecca sensor sits on UP's Sunset Route, a key corridor for intermodal containers moving eastbound from Southern California ports and the LA Basin toward destinations across the Southwest and beyond. This derailment — which involved an intermodal train itself — has directly disrupted this critical container flow.
Eastbound Containers by Type
| Container Type | Disruption Daily Avg | Baseline Daily Avg | Change |
|---|---|---|---|
| Container 20 Feet | 23.5 | 269.7 | -91.3% |
| Container 40 Feet | 101.0 | 1252.6 | -91.9% |
| Container 45 Feet | 0.0 | 9.1 | -100.0% |
| Container 53 Feet | 923.0 | 2350.6 | -60.7% |
| Tank Container 20 Feet | 0.0 | 2.9 | -100.0% |
| Trailer 20 Feet | 3.0 | 8.0 | -62.5% |
| Trailer 53 Feet | 15.0 | 71.1 | -78.9% |
Measured Disruption — Severe
Comparing the post-disruption period (Since Mar 19) against the 7-day pre-disruption baseline, RailState sensors recorded steep declines across all metrics at Mecca, CA:
Traffic Trends — Last 30 Days
Red bars indicate post-disruption days. Green line is the 7-day moving average.
Volume by Category
| Car Type | Disruption Avg | Baseline Avg | Change |
|---|---|---|---|
| Box Car | 11.0 | 259.7 | -95.8% |
| Flat Car | 34.5 | 486.3 | -92.9% |
| Gondola | 15.5 | 103.0 | -85.0% |
| Hopper | 28.5 | 311.6 | -90.9% |
| Intermodal | 966.5 | 3850.6 | -74.9% |
| Other | 3.0 | 10.9 | -72.4% |
| Tank Car | 41.0 | 331.6 | -87.6% |
| Train Type | Disruption Avg | Baseline Avg | Change |
|---|---|---|---|
| Automotive | 0.0 | 2.7 | -100.0% |
| Intermodal | 6.0 | 18.1 | -66.9% |
| Manifest | 1.0 | 8.4 | -88.1% |
| Passenger | 0.0 | 0.7 | -100.0% |
| Direction | Disruption Avg | Baseline Avg | Change |
|---|---|---|---|
| Eastbound | 4.0 | 13.9 | -71.1% |
| Westbound | 3.0 | 16.1 | -81.4% |
Why This Matters
For government agencies and emergency responders, RailState data provides an immediate, independent picture of what's on the tracks at a derailment site — including hazmat details — independently from railroad-provided data. When a train carrying ethanol and LPG derails near a medical center in a residential community, knowing the full consist in minutes rather than days can shape evacuation decisions and resource deployment.
For shippers and logistics operators, the ability to see a volume drop on a major corridor in near-real time means faster rerouting decisions, better customer communication, and reduced exposure to cascading delays.
For commodity and freight market analysts, RailState turns rail disruptions from anecdotal news events into quantified, data-driven signals. A sharp drop in container volume on a transcontinental corridor isn't a headline — it's a tradeable data point, available the same day.