A 2014 Wired article *What's Up With That: Building Bigger Roads Actually Makes Traffic Worse cites two economists:

In 2009, two economists—Matthew Turner of the University of Toronto and Gilles Duranton of the University of Pennsylvania—decided to compare the amount of new roads and highways built in different U.S. cities between 1980 and 2000, and the total number of miles driven in those cities over the same period.

“We found that there’s this perfect one-to-one relationship,” said Turner.


A more likely explanation, Turner and Duranton argue, is what they call the fundamental law of road congestion: New roads will create new drivers, resulting in the intensity of traffic staying the same.

Has this been thoroughly studied? Did they account for alternate routes, driving habits, amount of time drivers spent on road, not just how "full" the road was, etc?

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    – Oddthinking
    May 15, 2023 at 2:55
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    The answer to a general Q like the one in the title is always going to be "sometimes". Just consider any abandoned/ghost towns etc. (and their associated roads) as a counterexample. May 15, 2023 at 11:00
  • "Many studies have estimated travel demand elasticities, but one of the difficulties in interpreting these results is the uncertainty of the time frame that is applicable to the data. Another confounding problem is the ambiguity of the base of the observed elasticity; because most of the empirical cases observe a change in a small component of the total price of travel, the base for computing the percentage change in price is often not obvious and may not be given explicit treatment. The potential differences are large, e.g., a factor of three or more." May 15, 2023 at 11:23
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    "new roads create new drivers" - someone or someones were apparently studying microeconomics when they were supposed to be in that special "the birds and the bees" class in elementary school. May 15, 2023 at 20:25

2 Answers 2


Such claims ("resulting in the intensity of traffic staying the same") depend on a lot of things, the time frame and region considered most of all. A mini-review of prior work in the introductory part of a 2022 paper (I'm omitting the table of the studies) says:

The mentioned studies found vehicle distance travelled to lane length elasticities between 0.16 and 1.39, with most ratios are between 0.2 and 0.9 with various time periods, countries of origin and methodological considerations. High variation implies that induced traffic is indeed highly dependent on the circumstances, especially on existing network characteristics and the volume of latent demand.

Likewise a 2018 UK study/review:

The evidence reviewed in this study supports the findings of the SACTRA (1994) report that induced traffic does exist, though its size and significance is likely to vary in different circumstances. [...]

Findings for state level road networks in the US and the national Dutch network indicate an elasticity of around 0.2 across the whole road network, i.e. a 10% increase in road capacity could lead to 2% induced demand on the network. [...] Induced demand is likely to be higher for capacity improvements in urban areas or on highly congested routes.

That even mentions/summarizes the study in the Q:

Duranton & Turner (2011), use data from 228 Metropolitan Statistical Areas (MSAs) in the US and find large elasticities for IH demand with respect to IH capacity in the range 0.82 to 1.39, with the authors preferred value, 1.03, across all estimation methods used.


Pasidis (2017) determines elasticities for 545 Large Urban Zones (LUZ) in the EU28. Elasticities for the study period are found to be in the range 0.7 to 1.0. [...] Pasidis (2017) finds a much smaller elasticity for urban zones with metro systems (0.2), than for those without (0.72).

The review also mentions that the vast majority of case studies have been conducted in the US, where no alternative transportation methods generally exist on the scale/region considered. And regarding the studies that reported near 1 elasticity:

As they focus on particular road types, the demand response reported in these studies generally include re-assignment effects and is larger than the induced demand response.

Whereas studies that try to account for re-assignment calculate induced demand differently:

Case study evidence for the UK comes from Sloman et al. (2017) - who looked at a range of road improvements - and Rohr et al. (2012) who calculated induced traffic for the Manchester Motorway Box . Sloman et al. (2017) use a screenline approach to control for re-assignment and also report that they control for background growth. They calculate induced traffic as the percentage change in traffic flows, where traffic flows are based on trips (AADT or equivalent) and not distance travelled. As noted earlier, these measures are reported to be in excess of background traffic growth, which has been based on the average regional and county comparators over the same period. They found induced traffic for eight out of nine schemes in the range 5 to 10 per cent but 20 per cent for the M25. They report a short-run average increase of 7 per cent for seven schemes and a long run average of 47 per cent based on six schemes for which data were available 8 to 20 years after implementation.

And even for the US, results appear to differ drastically based on methodology:

Hymel, Small and Van Dender (2010) used 1966-2004 U.S. state-level cross-sectional time series data to evaluate how income, fuel price, road supply and traffic congestion affect vehicle miles travel (VMT). They find the elasticity of VMT with respect to statewide road density is 0.019 in the short run and 0.093 in the long run (a 10% increase in total lane-miles per square mile increases state vehicle mileage by 0.19% in the short run and 0.93% in the long run); with respect to total road miles is 0.037 in the short run and 0.186 in the long run (a 10% increase in lane-miles causes state VMT to increase 0.37% in the short run and 1.86% over the long run); [...] Their analysis indicates that long-run travel elasticities are typically 3.4–9.4 times short-run elasticities.

And that paper cites a meta-conclusion:

Induced travel effects generally decrease with the size of the unit of study – Larger effects are measured for single facilities while smaller effects are measured for regions and subareas. This is mainly due to diverted trips (drivers changing routes) causing more of the change on a single facility, whereas, at the regional level, diverted trips between routes within the region are not considered induced travel unless the trips become longer as a result.

And the time frame also matters, e.g. a 2022 MsC thesis on the US, which considers only more recent times:

I find that between 1980 and 2019, total lane miles increased by 13%, resulting in 8% to 24% more VMT. I also find that population growth results in 41% more VMT and rising per capita incomes result in 19% more VMT, driving most of the increase in vehicle miles traveled. Other factors contribute 7%, with an important portion of the increase unexplained.

  • Again, the OP asked about congestion, not traffic. If doubling the number of lanes results in 30% increase in drivers, that's more cars, but less congestion. May 16, 2023 at 18:24

According to a recent study by ADEME (French agency for ecologic transition and energy), the creation of additional traffic lanes has invariably led to an increase in traffic and, consequently, in the associated emissions. In other words, our behavior (frequency and number of car trips, car milage, etc.) tends to adapt to the road infrastructure offer as well as to the parking offer (number and usual availability of places and/or tariff).

Source : Mesures pour modifier le trafic routier en ville et qualité de l'air extérieur
Means to modify road traffic in the city and outdoor air quality (free download available)

Dans un deuxième temps, de multiples expériences mises en oeuvre à travers l’Europe pour diminuer le trafic routier en ville ont été recensées et analysées. Ce travail a permis de démontrer, chiffres à l’appui, que le trafic s’adapte à l’offre d’infrastructure routière. Des exemples précis sont donnés quant à la piétonisation, l’expérience des rues scolaires, les évolutions du stationnement, les aménagements de voiries visant à dissuader l’usager de prendre sa voiture…

Secondly (one of the sections of the report), multiple experiences implemented across Europe to reduce road traffic in cities were identified and analyzed. This work has made it possible to demonstrate, with supporting figures, that traffic adapts to the supply of road infrastructure. Specific examples are given with regard to pedestrianization, the experience of school streets, changes in parking, road developments aimed at dissuading users from taking their car, etc.

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    This is a vague answer. "Tends" just shows that there's some elasticity. But the original claim is stronger, "the intensity of traffic staying the same". Of course, it omits any time frame, so it's still a fairly vague claim. May 15, 2023 at 11:29
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    @Fizz: my answer is not a personal opinion, it's simply referring to a study. I have no real competence to comment it's conclusions or analyze the methodology.
    – Graffito
    May 15, 2023 at 13:34
  • My problem is that you're talking about an increase in cars, and the OP asked about an increase in congestion. May 16, 2023 at 18:21
  • @CristobolPolychronopolis: There are 2 main types of infrastructures, i.e. intercity highways and urban roads. The study concentrates on European urban areas where there is already a lot of congestion. Therefore, traffic increase translates into further congestion.
    – Graffito
    May 16, 2023 at 21:15
  • @Graffito But the claim seems to be that congestion (not traffic) is increased overall. Certainly if you measure congestion at the moment the additional infrastructure is opened, it will increase with traffic over time, but if the difference of the traffic before and after the improvement is less than the increase in capacity, the traffic has increased but the congestion has decreased. If I put 1 liter of water into a 1 liter container, it's full; if I put that water plus another liter into a 3 liter container, I have more water, but the container is less full. May 17, 2023 at 14:54

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