Update on Project Activities
This week, we continued working on the final report, presentation, and map. We put together a literature review on Level of Traffic Stress based on the original Mineta study, and we will also created a summary of the other major bikeability metrics. We have also written up the case study on Long Beach and will do so for Copenhagen as well. For the map, we added the intersection analysis from the Mineta study and displayed the results as points. We also tested out ArcGIS Online as an alternative to Carto, and found that it has similar capabilities and is easier to use.
What We Observed and Learned
The original LTS uses four components to determine intersection comfort. Firstly, signalized intersections are automatically designated as LTS 1. For unsignalized intersections, a score is assigned based on the presence of a median refuge, number of lanes being crossed, and the speed limit of the street being crossed. However, we found that almost all intersections in San Francisco are LTS 1 or 2 based on this approach, because most are signalized or have low speed limits. This scoring is not reflective of the actual stress of many intersections, which lack protection, markings, or bicycle signals. By applying lessons from Long Beach’s intersection treatments (bike boxes, markings, detection signals) and practices we saw in San Francisco (protected intersections), we hope to redo the analysis to better reflect reality.
Critical Analysis/Moving Forward
Using metrics to gauge a City’s well-being or progress can be tricky business. What works for one study does not always work for another. In the case of the Mineta study, intersection analysis was used for an academic research paper on network connectivity, which requires advanced software and datasets that can be easily analyzed. Meanwhile, the SFMTA uses LTS to help decide on new infrastructure investments, which may require more detailed information about each intersection and what treatments already exist. As we continue improving on our version of LTS, we have to keep in mind that the SFMTA uses LTS for important decisions, and that our LTS should be an accurate reflection of real bicycling conditions.
Using Carto also taught us that coding and web design can be powerful planning tools. Interactive online maps can communicate information to the public and other government departments in a clear, engaging format. However, we should keep in mind that maps have also been used to target vulnerable populations. For our project, including metrics like income level and car ownership can help planners understand which neighborhoods might desire more investment in bicycling. At the same time, we must take care to interpret our map in a way that is sensitive to existing residents. For example, some people may view bike lanes as a cause of gentrification, rather than a way to make mobility more affordable. Thus, the socioeconomic aspects of our metric should be viewed with caution.