Access Across America: Auto 2015

CTS Catalyst September 2016 just came out, and announces our Access Across America: Auto 2015 study: Study estimates accessibility to jobs by auto in U.S. cities. The article is reprinted below:

Map of Accessibility to jobs by auto in U.S.
Accessibility to jobs by auto

A new report from the University’s Accessibility Observatory estimates the accessibility to jobs by auto for each of the 11 million U.S. census blocks and analyzes these data in the 50 largest (by population) metropolitan areas.

“Accessibility is the ease and feasibility of reaching valuable destinations,” says Andrew Owen, director of the Observatory. “Job accessibility is an important consideration in the attractiveness and usefulness of a place or area.”

Travel times are calculated using a detailed road network and speed data that reflect typical conditions for an  8 a.m. Wednesday morning departure. Additionally, the accessibility results for 8 a.m. are compared with accessibility results for 4 a.m. to estimate the impact of road and highway congestion on job accessibility.

Map of U.S. showing reduced job accessibility due to congestion
Reduced job accessibility due to congestion

Rankings are determined by a weighted average of accessibility, with a higher weight given to closer, easier-to-access jobs. Jobs reachable within 10 minutes are weighted most heavily, and jobs are given decreasing weights as travel time increases up to 60 minutes.

Based on this measure, the research team calculated the 10 metropolitan areas with the greatest accessibility to jobs by auto (see sidebar).

A similar weighting approach was applied to calculate an average congestion impact for each metropolitan area. Based on this measure, the team calculated the 10 metropolitan areas where workers experience, on average, the greatest reduction in job access due to congestion (see sidebar).

Areas with the greatest loss in job accessibility due to congestion

  1. Los Angeles
  2. Boston
  3. Chicago
  4. New York
  5. Phoenix
  6. Houston
  7. Riverside
  8. Seattle
  9. Pittsburgh
  10. San Francisco

Metropolitan areas with the greatest job accessibility by auto

  1. New York
  2. Los Angeles
  3. Chicago
  4. Dallas
  5. San Jose
  6. San Francisco
  7. Washington, DC
  8. Houston
  9. Boston
  10. Philadelphia

“Rather than focusing on how congestion affects individual travelers, our approach quantifies the overall impact that congestion has on the potential for interaction within urban areas,” Owen explains.

“For example, the Minneapolis–St. Paul metro area ranked 12th in terms of job accessibility but 23rd in the reduction in job access due to congestion,” he says. “This suggests that job accessibility is influenced less by congestion here than in other cities.”

The report—Access Across America: Auto 2015—presents detailed accessibility and congestion impact values for each metropolitan area as well as block-level maps that illustrate the spatial patterns of accessibility within each area. It also includes a census tract-level map that shows accessibility patterns at a national scale.

The research was sponsored by the National Accessibility Evaluation Pooled-Fund Study, a multi-year effort led by the Minnesota Department of Transportation and supported by partners including the Federal Highway Administration and 10 state DOTs.


Related Links

Demand for Future Transport

There are differing beliefs about the effects of autonomous vehicles on travel demand. On the one hand, we expect that automation of itself is a technology that makes travel easier, it pushes the demand curve to the right. For the same general cost, people are more willing to travel. Exurbanization has a similar effect (and automation and exurbanization form a nice positive feedback system as well).

Demand vectors for vehicle travel in a changing technological and socio-economic environment.
Demand vectors for vehicle travel in a changing technological and socio-economic environment.

On the other hand, the move from private vehicle ownership to mobility as a service, which is likely in larger cities means that the marginal cost of a trip might rise from very low (since the vehicle is already owned) to high (since the cost of the vehicle has to be recovered on a per-trip basis). This moves the demand curve to the left. It is similar in effect to urbanization (and urbanization and mobility-as-a-service form a nice positive feedback system). Lots of other changes also move the demand curve to the left, including demographic trends, substituting information technologies for work, socializing, and shopping, and dematerialization.

Income moves the willingness to pay for the same amount of travel up or down.

Changes in the price structure of travel move along the demand curve as shown here.

This is one scheme for thinking about the effects of new technologies on travel demand (which we will introduce in the . How these vectors net out is a problem that could be solved with analytical geometry, if only we knew their relative magnitudes. In The End of Traffic and the Future of Transport, we argue demand in the US is generally moving a bit more to the left than the right (though the last year saw sharp reductions in fuel costs and higher incomes and thus moved us more to the right than the left). But we also note that new automation technologies change the available capacity of roads through improved packing of vehicles in motion and smaller vehicles. Less demand plus more supply reduces congestion effects in the net.