Not necessarily – in fact in the US, not even usually!
It’s a truism that denser, more concentrated cities tend to have higher public transport use. Various studies have confirmed this intuition but what is usually left unexamined is the implicit assumption that such cities consequently have lower car use.
This study of 31 of the largest cities in the US found that assumption is not correct. Higher public transport mode share does not translate on average to lower kilometres of travel by car, shorter commutes by car, or lower levels of traffic congestion.
The primary finding “is that land use, at least at the aggregate level studied here, is not a major leverage point in the determination of overall population travel choices”.
Undertaken by Gary Barnes from the Centre for Transportation Studies at the University of Minnesota, the research found that, if anything, “the higher densities that increase transit share tend to increase commute times and congestion levels”.
The main objective of the project was to identify the effect of land use on travel behaviour. Most studies concentrate on the effect of average density on one or two variables, usually transit share and sometimes total kilometres of car travel.
Barnes’ approach was much more extensive. For each urbanised area, he defined 15 descriptors of travel behaviour, 11 of land use and 15 other, mainly demographic, factors. Moreover, he employed the concept of ‘weighted density’ (he calls it ‘perceived density’) to more accurately describe the distribution of both population and employment in each city.
Barnes confirms that residential concentration increases transit’s share of travel, but he notes the effect is not large. Contrary to the underlying assumptions of much urban policy:
Even very large changes in land use have very little impact on travel behaviour, in good ways or in bad. Apparently the larger effects sometimes observed in neighborhood-scale studies are just that: neighbourhood-scale effects that do not extend their benefits to the larger urbanized area.
His analysis implies that increasing residential density by 100% would increase transit share by only 5-6%. To get a 1% increase in walking and cycling’s combined mode share would require an increase in residential density of 5,000 persons/mile2 (1,931/km2). Similarly, a 14% increase in density would only yield a 0.5% decrease in in-car travel time per person. Read the rest of this entry »
The accompanying chart shows how public transport’s share of the journey to work varies with population density across 41 US and Australian cities.
It is taken from the same article that I mentioned in my last post. The authors, Dr John Stone and Dr Paul Mees, find there is only a modest relationship between population density and transit share (R2 = 0.229). They conclude that “higher density across the whole urban region is not the explanatory variable that many might expect”.
Los Angeles, for example, is the densest metropolitan area in the US – denser ever than New York – yet the chart shows public transport’s share of work travel in LA is much smaller than in NY.
If that seems counter-intuitive, your intuition could be right. The chart uses average population density calculated across the entire urbanised area of each city.
While that’s perfectly alright in some contexts, it doesn’t allow for the possibility that public transport’s ability to win travel away from cars is related to the morphology of density – the ‘peaks and troughs’ in the way the population is spatially distributed. It’s possible that the relative proportion of population in high density areas vs low density areas has a greater impact on mode share.
Using average density probably won’t present a serious problem with cities like Atlanta, Austin, Dallas, Phoenix and Portland where the population is overwhelmingly suburbanised at relatively uniform (low) densities. But it could have a big impact on places like New York which have an extensive ring of low density suburbs as well as a high density central region e.g. Manhattan and Brooklyn.
A way of dealing with this issue is to use weighted density rather than average density. This involves weighting the density of each suburb (or other convenient geographical unit e.g. traffic zone) by its share of the city’s total population. So a one km2 suburb with 5,000 residents (say) carries a lot more weight than another suburb of the same area that has only 1,000 residents. Read the rest of this entry »