By Brendan Murphy, Lead Researcher, Accessibility Observatory, University of Minnesota
The Accessibility Observatory is primarily concerned with—as you may imagine—measuring accessibility to destinations using our transportation systems. A good portion of our research and studies pertain to measuring accessibility by public transit and automobile. This is not without good reason—the vast majority of North Americans commute to work and other destinations by car or transit, although mode shares do vary across regions and among different cities in the U.S. However, people are steadily increasing the rates at which they choose to bike to where they need to go in cities, and with that comes the need to focus more intently on whether our road, trail, and path systems do a good job (or not) of getting people on bikes to destinations safely and efficiently.
At the Observatory, we focus on measuring transportation systems with readily understandable metrics of accessibility. For example: from the corner of SE Oak Street and SE Washington Avenue on the University of Minnesota campus, a commuter can reach an average of 220,169 jobs—by walking and using transit—within 30 minutes of travel time during the morning 7–9 a.m. commute. If someone only has 15 minutes of available travel time, that number reduces to 31,577 jobs; an hour affords access to 616,053 opportunities (although obviously not all job opportunities are both available and appropriate to individual people—equity is a conversation for another time). To produce these metrics, we calculate how long travel takes in real-world conditions, subject to actual bus and rail transit schedules and to automobile congestion during peak periods; we then count up the job opportunities at all places reachable within the travel time budget.
Evaluating real-world bicycle access to destinations is a significantly more challenging problem, since route planning by bicycle is significantly more sensitive to things like road type, speed limit, hills, and the presence (or lack) of dedicated infrastructure like bike lanes than route planning for cars or transit. The above map shows bicycle accessibility in the Twin Cities area, without accounting for where people actually would prefer to ride or avoid; the reality is likely significantly less geographically uniform.
D.C.-based consulting group Conveyal have written about this fundamental difference between traveler behaviors while biking vs. using most other modes. Defining the routes, roads, and facilities that people are willing to actually use has a significant effect on how many destinations they can reach within a given time. Among people who are interested in bicycling more often, many are dissatisfied with existing infrastructure and the desire for more protected bike lanes is a unifying factor among them.
To account for this sensitivity among people interested in bicycling, the Observatory is working to adapt the bicycle level of traffic stress (LTS) framework to our research. LTS assigns a “stress level” ranking to roadways based on data such as street width, number of lanes, speed limit, the presence of bike lane facilities, and other factors. The higher the stress level, the less likely people are to use the street for biking and the less likely people will be able to reach businesses and other destinations on that roadway by bike.
Incorporating LTS allows us to systematically include or eliminate individual roadways in the “bike-friendly” subset of roads that people would be willing to use and to allow for real-world metrics of bicycle access to destinations. A challenge in this area is the lack of unified, available data sources that give the inputs required for LTS evaluation—namely speed limits, lane configurations, and intersection geometries. Many of these details must be manually surveyed in LTS studies. The Twin Cities serve as the living doorstep laboratory for the Observatory’s research, so while conducting a full LTS survey is possible, including other metropolitan areas for comparison becomes impossible without some type of shortcut. To approximate the rankings, we are working on developing a heuristic similar to the system Conveyal implemented, which can rely strictly on open-source, available OpenStreetMap data.
What we will get out of this process is incredibly useful: not only will we have much more accurate metrics of access to destinations by bicycle, but we will also be able to easily see how small changes in the bicycle network affect accessibility. If a specific path is closed for repair or a new bike lane or protected bicycle facility is installed, then people will alter their bicycle routes and accessibility will change accordingly. If a specific area of the city is less well-connected for bicycle travel, new facilities can be simulated in software, and the accessibility gains from specific bicycle infrastructure projects may be estimated. This could inform strategic placement of bicycle infrastructure connections where they are most needed for improving connectivity and access to useful and attractive places.