Congestion, accessibility, and the future of transportation metrics

Congestion, accessibility, and the future of transportation metrics

By Andrew Owen, Director, Accessibility Observatory

Last month, the Texas A&M Transportation Institute released its 2015 Urban Mobility Scorecard, the latest in an occasional series of reports focused on automobile congestion in U.S. metropolitan areas. Using data from INRIX, these reports estimate the costs of congestion, represented by the number of “extra” hours that automobile commuters spend by traveling at low, congested speeds instead of high, uncongested speeds. The implication is that our cities function best when they allow cars to move fast.

Similar information is available from the TomTom Traffic Index. TomTom’s global look at congestion indicates that only one U.S. city—Los Angeles—makes the top 10 list of most congested cities in the world, and only five U.S. cities make the top 50. However, methodological differences mean that results from the Urban Mobility Scorecard and the TomTom Traffic Index are not exactly comparable. It is interesting to compare the two studies as examples of different ways to represent the impact that congestion has on travelers.

Detailed congestion data are a critical component of our work at the Accessibility Observatory. But for us, automobile congestion is only part of the whole picture. We approach all of our research and evaluation projects with the understanding that all travel is motivated by a desire to reach destinations, and that no study of transportation is complete unless it looks at both the costs and benefits of travel. Traffic speeds are important not because people are happier when they can drive fast, but because speeds, along with trip distance, determine how long it takes travelers to drive to their destination. Accessibility metrics let us measure this outcome directly, rather than relying on congestion as an incomplete proxy.

Additionally, we find it valuable to take a multimodal approach to evaluating transportation systems—and that means using metrics that can be applied meaningfully across many transportation modes. Our Access Across America series of reports demonstrates how a single accessibility metric can be applied to evaluate commuting by driving, transit, and walking throughout the U.S. This allows us to directly compare the impact that these different transportation systems have in connecting people to destinations that matter.

We think that a transportation planning process that incorporates accessibility metrics will enable smarter and more effective investments and operational decisions. In our National Accessibility Evaluation project, we are partnering with organizations that are ready to look beyond congestion to develop a new national scorecard that measures what really matters in transportation: getting people where they want to go by providing access to destinations.

Many types of organizations are invited to join this pooled-fund project, including state DOTs, MPOs, county and municipal governments, and transit agencies. For information about the project or to find out how your organization can participate, use the Lead Agency Contact information provided on the official project information page or contact Accessibility Observatory staff.

Circuity in Urban Transit Networks

Recently accepted

How transit circuity declines with distance, and is lower for real transit trips than random trips, or real auto trips.
How transit circuity declines with distance, and is lower for real transit trips than random trips, or real auto trips.

This paper investigates the circuity of transit networks and examines auto mode share as a function of circuity and accessibility to better understand the performance of urban transit systems. We first survey transit circuity in the Minneapolis – St. Paul, Minnesota region in detail, comparing auto and transit trips. This paper finds that circuity can help to explain mode choices of commuters. We then investigate thirty-five additional metropolitan areas in the United States. The results from these areas show that transit circuity exponentially declines as travel time increases. Moreover, we find that the circuity of transit networks is higher than that of road networks, illustrating how transit systems choose to expand their spatial coverage at the expense of directness and efficiency in public transportation networks. This paper performs a regression analysis, which suggests that the circuity of transportation networks can estimate transit accessibility, which helps to explain mode share.

Key words: circuity, accessibility, transit networks, network efficiency, mode share, public transportation