The Data is Just Sitting There — Let’s Use It!

One of the persistent themes of Bacon’s Rebellion is that human settlement patterns influence the demand for new roads. Some of us have argued that scattered, disconnected, low-density development patterns force motorists to make more car trips and drive greater distances — thus putting more stress on the transportation system — than their counterparts living in more compact, more balanced and better designed communities.

While most people commenting in this blog accept the idea that the pattern and density of development has some impact on travel, some disagree that it’s a significant factor. In the end, everybody’s arguments go around in circles because no one can produce “conclusive” evidence one way or the other.

Maybe it’s time to start gathering “conclusive” evidence. Building upon an idea suggested by Chris Miller, president of the Piedmont Environmental Council, I would propose a two-part initiative.

(1) Categorize every census block in Virginia by its dominant development pattern, accounting for variables such as population density, building type, street pattern (grid street, cul de sac, whatever), presence of mass transit and other salient characteristics. (I’m open to ideas on which key variables should be considered.)

(2) Then append to the list of census blocks these two data sets: (a) Census commuting data, and (b) Division of Motor Vehicle data on vehicle miles driven by every licensed driver residing in the census tract.

That relatively simple exercise should provide the data to answer once and for all the question whether certain settlement patterns generate more automobile traffic than others — and by how much.

Given the General Assembly’s new-found interest in measuring the impact of new development upon traffic, the findings of such a study would prove extremely useful to everyone from academic researchers to local planning offices, from metropolitan planning organizations to VDOT.

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4 responses to “The Data is Just Sitting There — Let’s Use It!”

  1. Jim Wamsley Avatar
    Jim Wamsley

    The Transportation community calls the tracks “Transportation Analysis Zones” and uses a “one day” travel survey to determine the miles traveled. You are adding refinements to the data set that already exists.

    No matter what data set you use, those who believe in “Faith Based” decisions will not accept your answers.

  2. Ray Hyde Avatar
    Ray Hyde

    “The average length of a commute trip increased by 26% from 8.5 miles in 1983 to 10.7 miles in 1990 while the commute time
    increased by a substantially lower rate of 10% during the same period. The increase in
    commute distance may partially reflect the continued development of suburban and exurban residential areas. The resulting commutes are longer but are travelled at faster speeds. In terms of the number of miles travelled per hour, commuting speeds for all areas improved between 1983 and 1990.”

    National Personal Transportation Survey.

    These are old numbers but the indications are there for anyone to see. Whether they are significant or not is strictly a value judgement, I think.

    In central city areas the average trip distance increased from 7.5 miles to 8.8 miles from 1983 to 1990. The time increase was from 16.8 to 17.2 minutes.

    For those not in the central area distance increased from 9.2 to 11.6 miles and time from 18.4 to 20.3 minutes.

    For those not in a metropolitan statistical area, trip lengths went from 7.0 to 10.6 miles and travel time from 14.3 to 16.3 minutes.

    The travel speed in the three zones in 1990 were 29.5 in Central city, 34.3 not in central and 37.8 not in MSA.

    These travel speeds strongly suggests we have no need for 300-400HP autos.


    “The Impact of Residential
    Density on Vehicle Usage and
    Energy Consumption” (February 2005)

    Thomas F. Golob and
    David Brownstone find that

    “Comparing two households that are similar in all respects except residential density, a lower density of 1,000 housing units per square mile implies a positive difference of almost 1,200 miles per year and about 65 more gallons of fuel per household. This total effect of residential density on fuel usage is decomposed into to two paths of influence. Increased mileage leads to a difference of
    45 gallons, but there is an additional direct effect of density through lower fleet fuel
    economy of 20 gallons per year, a result of vehicle type choice.”

    They also point out that choice of larger vehicles and choice of less dense living areas is associated with income level.

    The data is dependent on one day trip diaries, and on odometer reading taken a few months apart.
    Also 65% of the California population studied lives at densities greater than around 7000 persons per square mile.

    At the highest densities (more than 5000 residences per sq mi) annual mileage is still over 15k miles per year. those at the lowest densities (less than fity residences per sq mile) travelled 22k miles per year, which is about the same for 500 residences per sq mile and 2000 residences per sq mile. From 2000 residences per sq mile to more than 5000 residences per sq mile the mileage decreases from 22k miles per year down to 15k miles.

    However, there is a big spike for those that live at densities of around a hundred units per sq mile, and these driver rack up over 32,000 miles per year. they account for around 5% of the population.

    In other words, you could save 2.5% of annual mileage if you convince the wealthiest individuals to move from the far exurbs to the most densely populated areas.

    If you move the people who live at densities of 700 units per Sq mi you would save another 5%, and if you moved the people that live at 2000 units per sq mile you could pick up another 8%.

    Doing that would reduce miles driven by 15% and just about double the number of miles driven per square mile for the most dense areas. The theory is that this will reduce congestion.

    Good luck.

  3. James Atticus Bowden Avatar
    James Atticus Bowden

    One problem, the half life of the accuracy of that data is short. If it’s census data it is already OBE. On the other hand if the patterns are general enough that the ‘eaches’ don’t skew the results (depends on the math) then it might be interesting to see what is what.

  4. Ray Hyde Avatar
    Ray Hyde

    A recent editorial in Science magazine was entitled “Environmental Science adrift in the Blogosphere” (April 2006)

    It pointed out the wide variety of environmental claims that are made on blogs that are not in synch with the best current science.

    “If environmental scientists ignore such online communication platforms such as weblogs, we run the risk of creating a generation of eco-illiterate consumers and voters at a crucial time for earth’s diminishing resources.”

    The article surveyed a number of weblogs and concluded that blogs are overstating the incidence of species extinction by a factor of two to one over the best “scientific” estimates.

    I happen to believe that any extinction is one too many, but that is beside the point.

    The article that scientists actively engage in blogging in order to increase the presence of informed opinions in the blogosphere.

    As a trained environmental scientist and engineer, I concur. Who knew that I might be a small part of the leading edge in socio-environmental thought?

    The article goes on to say that blogs offer a new mdium for publicizing ideas, brainstorming, and generating ideas. Additionally weblogs could be used for disseminating and interpreting peer-reviewed literature, and for gaining early feedback in the early stages of research.

    There are obvious spontaneous and political currents in blogging that are antithetical to careful research, for which I have frequently been taken to task.

    I wonder what the weblogs might have said about Copernicus, in his time.

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