The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates.
I often am asked why I write for FreightWaves when I am a railway man. There is a simple answer. If you want to beat your competitor, do not spend too much time looking internally at only the railway part you already know. Instead, keep your eyes on what the lead dogs are doing.
Writing for FreightWaves gives a rail journalist a strategic advantage, because FreightWaves uses near-real-time digital age traffic analysis techniques.
Like logisticians, FreightWaves follows the trucks and other patterns. It does it every day. In contrast, railroad market viewing often takes a month or longer. The modern tools used by FreightWaves and other digital watchers are using interesting graphics and templates — in near-real-time mode.
Thus, with FreightWaves, carriers, shippers and others can match and compare their internal inventory data systems to the growing terabytes of broad domestic and even international data intelligence.
Even government policy and or regulatory agencies can use prepared intel templates offered by multiple data scientists as vendor subscriptions.
Let us examine some of the data pictures as of the end of November through the beginning of December, starting with this incredible 3D-like map of trucking demand.
Despite dealing with thousands of independent daily truck moves, companies like FreightWaves have figured out how to uniquely display such freight movement patterns.
Unfortunately, similar market views are not yet available for rail carload market views. Not yet.
As a columnist, I get to network and participate in the development with FreightWaves colleagues. Thus, I get to see how such intel gets used.
If you are a financial investor, data scientists show us how volume and linked revenues are trending. That makes projections of pro forma income statement analysis more accurate.
If you are a transport planner in the federal agencies or the various state DOTs, you can compare traffic counts along key corridors with truck origin/destination pairs in more than 100 lanes.
For yesterday’s movements. Incredible!
The significance is that modal competition, congestion analysis and even sophisticated pricing analytics are rapidly possible using smart proprietary algorithms.
Here are some other data analytic views.
The first illustrates time-based changes in freight charges for loaded containers moving between China and North American western ports. It shows the difference between 2019 and 2020 rate levels.
Here is a Dec. 5 report that dissects changes in truckload spot rates in the long-haul San Francisco to Philadelphia paired market, where recent rates have spiked to the $10,000 range.
That produce market once was owned by the railroads. Not anymore.
Interestingly, some consultancy firms have in a way partnered with FreightWaves to make their own graphics more robust.
For example, Susquehanna Financial Group (SFG) publishes a weekly report called the global freight chartbook. The chartbook provides a near-real-time look at volume, pricing and other supply/demand data for both international and domestic freight markets.
Instead of inventing all-new graphic tools, SFG has adapted some of the FreightWaves tools and graphics.
Below is a simple example of how SFG uses multiple datasets to compare changes in trucking with changes in rail intermodal.
The graph demonstrates that the rail intermodal recovery slope is not keeping pace with selected truckling recovery.
Who should be looking at these patterns?
Beyond shippers, probably the U.S. Department of Transportation, the Federal Railroad Administration and the Surface Transportation Board. Plus a few state DOTs.
How can they keep up with changes in the market without such intelligence?
Using other datasets, SFG combines weekly and multiple-years trending data for domestic carload freight.
Here is an interesting example of a current trend line where companies like SFG use an indexing calculation set to the first quarter instead of the beginning of the year.
That one index time point change shows the viewer a different post-pandemic rail carload recovery slope rate. This graph shows a 15-year pattern.
This SFG image displays changes in selected rail carload merchandise traffic. This is primarily intermodal and auto traffic.
Because of the index period change, it is a bit surprising to see how high the 2020-line slope is.
With these analytics, SFG subscribers can see how certain commodity and industrial rail traffic has recovered from Q2’s year 2020 losses and is now tracking at the low end of normal seasonal trends.
The old ways of looking at freight are changing.
What’s troubling is that my railroad sector is not yet fully engaged in these intelligence tools. Nor are many of the public sector bodies.
Superior market intel can be a game changer.
It is fundamentally a smart software play. It is not a burdensome capex investment. Railroads can enter quickly by leasing or buying the market intel.
Do you concur? Contrary opinions are welcomed.
The market view interpretation of these graphics is Jim Blaze’s. The actual graphic techniques are from FreightWaves and other parties, including SFG.
Technical conclusions and interpretive market conclusions asserted above might not accurately reflect interpretations offered by data scientists like FreightWaves’ Zach Strickland or SFG’s Bascome Majors.