A Reporter’s Guide to ‘Congestion Cost’ Studies
8:04 AM PST on January 17, 2023
This article originally appeared on City Observatory and is republished with permission.
Every year or so, one or more traffic-counting organizations trots out a report claiming that congestion is costing us tens of billions dollars each year. Despite the “big data” and elaborate estimates, the results are simply bunk, because they’re based on a flawed premise.
Each of these reports calculates as the “cost” of congestion how much longer a trip takes at peak hours compared to off-peak hours, but fails to define what actions or policies could produce such a change in traffic, and how much they would cost. Every one of these reports tallies up the supposed “costs” of congestion, without telling how to solve the problem or what it would cost.
Traffic lights cost billions
You can apply this idea of computing a “cost” to any kind of waiting. (We’ve even done it, tongue-in-cheek, but calculator in hand, for cappuccino.) Others take this notion seriously. For example, crack statisticians at the University of Maryland have sifted through reams, nay, gigabytes, of big data, and have produced a comprehensive, nationwide estimate of the amount of time lost when we sit, waiting for red lights to turn green.
According to those University of Maryland estimates, time lost sitting at traffic signals amounts to 329 million vehicle hours of delay, and costs us $8.6 billion dollars per year. They estimate that time spent waiting at traffic signals is roughly three-fifths as great as the 561 million vehicle hours of delay associated with routine “recurring” traffic congestion.
This University of Maryland study calculates that roughly 19 percent of all traffic congestion is due to waiting at traffic signals. And yes, those traffic lights do get in your way and slow you down:
Traffic signals cause delays as vehicles queue at intersections. In 10 states, traffic signals are the top cause of traffic congestion, though congestion levels overall remain relatively low in those states. For example, even though Alaska ranked highest in the country in percentage of delay caused by signals at 53%, it ranked 42nd in terms of total hours of delay caused by signals.
As an accounting exercise, there’s little reason to doubt these calculations. But whether they constitute a “loss” is highly doubtful, because there’s no question that we’d all collectively lose more time in travel if there were no traffic lights.
The policy implication of this finding is not that we should be tearing out or turning off traffic signals. That would be absurd, of course. And what the claims of time spent waiting at traffic lights constitute an actual “loss” rests on the assumption that there’s some other traffic-light free way of managing the flow of traffic at intersections that would involve less total travel time for those now waiting. Simply getting rid of traffic lights — and, say, replacing them with stop signs — would likely decrease the throughput of many intersections and actually increase delays (though it might beneficially reduce traffic speeds and improve safety for vulnerable road users). Theoretically, one might replace every single traffic light in the US with a fully grade separated interchange without stops.
Let’s suppose, for a moment, that you could instantly replace all of the 330,000 or so traffic signals in the US with grade-separated interchanges that eliminated traffic signals. That might eliminate all the time “lost” by vehicles waiting at traffic lights, but it would come at a cost. At say, $10 million per intersection (which is probably a conservative estimate) that would cost about $3.3 trillion, all that to save maybe $8.6 billion per year. Time spent waiting at traffic lights is costly, only if you ignore the vastly greater cost of doing anything to try to reduce it.
It’s easy to point out that the theory about the “time loss” due to traffic lights is pretty silly. But what’s true of the elaborate (but fundamentally wrong-headed) estimates of the time “lost” to traffic signals is that it also holds for all the other estimates of supposed congestion costs. For years, a range of highly numerate charlatans have been purporting to compute the value of time lost to traffic congestion. The congestion cost studies generated by the Texas Transportation Institute, Inrix, Tom-Tom and others invariably conclude that traffic congestion costs us billions of dollars a year. Their copious data creates the illusion of statistical precision without providing any actually useful knowledge.
They generate heat, but don’t shed any light: The congestion cost estimates are part of the propaganda effort of the road-builders, who assert we need to spend even more billions to widen roads to recoup these losses.
It’s an example of a measurement that’s literally true, but quite meaningless. It’s true in the sense that people probably do spend millions of hours, collectively sitting at traffic lights or traveling more slowly because of congestion. It’s meaningless, because there’s not some real-world alternative where you could build enough road capacity to eliminate these delays.
So, as an elaborate accounting exercise, you can use big data and computing power to produce this estimate, but the result is a factoid that conveys no useful, actionable information — just as we’ve shown with our Cappuccino Congestion Index, which totes up the billions of dollars American’s “lose” waiting in line at coffee shops.
Where are my flying cars?
The sky’s the limit if you want to generate large estimates of the supposed time “lost” due to slower than imaginable travel. Consider for example flying cars, which according to this year’s Consumer Electronics Show, are just about to darken our skies. One company, Aska, is showing a four-seat prototype that can whisk you and a friend at speeds of up to 150 miles per hour, land in the space of a helipad, and park in an area no larger than a conventional parking space.
Unsurprisingly, the flying car advocates are pitching it as a solution to traffic congestion (move over Elon Musk):
Who doesn’t want to hop over the traffic? The Aska A5 can fly at a maximum speed of 150 mph and travel 250 miles on a single charge. That could cut a 100-mile car trip down to just 30 minutes. Aska’s Kaplinsky sees the A5 flying car tackling long commutes, allowing them to move to more affordable communities further away from big cities and reduce the number of regular cars they own, he said, adding that most people would probably use them when needed through a ride-sharing service.
If you could travel by flying car to all your destinations, it would shave hours a day off your total travel time. Imagine all the time we could save if everybody had a flying car, and what those savings would be worth. With a spreadsheet and some travel data, you could work out an estimate of how many million hours might be saved and how many tens or hundreds billions of dollars that saved travel time would be worth. You could produce a report arguing that the personal flying car shortage costs us in lost time and money.
It would be a large but meaningless number, because there’s no world where its financially feasible, much less physically possible, for everyone to take every trip by flying car.
The price per flying car is a cool $789,000 (plus operating costs), and there aren’t enough heliports or heliport-adjacent landing spots to accommodate everyone; not to mention that there’s no air traffic control system for thousands of such vehicles moving over cities. The only way to make meaning of such numbers is in the context of plausible, real-world alternatives.
And that’s exactly what these cost of congestion studies almost invariably fail to consider. Something is only a “cost” if there’s an actual practical alternative that would save the lost time without incurring even greater monetary costs in doing so. Imaginary savings from an impossible, or impossibly expensive alternative aren’t savings at all.
It’s tempting to believe that more data will make the answers to our vexing problems, like traffic congestion, clearer. But the reverse is often true: an avalanche of big data obscure a fundamental truth. That’s what’s going on here.
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