People + Data: Solving Public Transit
San Francisco is blessed with talented people itching for a chance to help make things better. It seems like we should be able to solve all our problems if we could just get these people together in the same room with the right resources. Fortunately, many such folks were interested in spending a Saturday together to work on solving public transit.
The advocates of San Francisco Transit Riders got the opportunity to partner with the American Planning Association (APA) during their annual conference. The APA planned a Data Jam to bring planners together with civic hackers to see what the two disciplines could learn from each other. We were fortunate that the civic hackers of Code for America (CFA) were willing and able to host the day-long workshop.
Not surprisingly, CFA’s volunteer Brigade has a large contingent interested in finding solutions to our chronically frustrating Muni service. It seems it should be so easy to solve. If only!
At the convergence of these varied transit-oriented urbanists — planners, advocates, and civic hackers — creative problem solving was in full force. At a happy hour hosted at Arup, we chatted freely about a whole array of transit challenges:
- Paying for transit — from free fares to large infrastructure projects
- The intersection of transit, housing, and jobs in San Francisco
- The complexity of our multitude of Bay Area transit agencies, schedules, and fares
- How does transit work with other mobility options — what are the relative strengths, the use of space, etc.
- Using data and mapping to optimize routes for fast, frequent, reliable, large ridership
- Transit-only lane enforcement
- Getting non-transit-riders on transit, or at least to care about it
- How to restructure parking policies and curbs to make room for transit
An overarching challenge was to identify data-based tools that would help with San Francisco Transit Riders’ 30x30 vision: what routes lend themselves to having rapid service travel from end-to-end in 30 minutes by 2030? How do you define rapid service? Where would it be most useful?
So, on a beautiful Saturday in SOMA, we rolled up our sleeves and got to work. Planners and hacktivists huddled together in smaller groups to discuss the challenges and work on solutions and tools.
Visualizing Travel Time
Oftentimes, agency outreach is hard to digest and doesn’t communicate the real costs and benefits of various projects. A list of stops removed or altered doesn’t clearly convey what the results can be.
So one team built a tool to show how far you can get in 30 minutes on Muni — a tool that can compare your 30-minute reach depending on if you get on a local bus or a rapid one.
Identifying Ideal Rapid Stops
One group looked at what stop removal looks like in terms of the cost in human-seconds. If X number of humans use a stop on average, and if that stop is removed so they walk a bit further, what does that cost in their very human time? What’s the benefit to riders on the bus, saving precious seconds by not stopping at a given stop?
Another group looked at stop removal on one section of one route, to discuss the on-the-ground realities of which stops could be removed. They looked at things like transfer points, features of commercial corridors, and the existence of hills.
Team Bus Grade
This team drilled down to specific bus stops to shed light on how each route is doing at given stops. Their prototype app shows on-time performance, a grade based on headways (the time between buses), the worst delay for that route at that stop, and the average speed.
Identifying Travel Time
Another group assessed how long, on average, Muni’s various routes take to travel end to end, using OpenTripPlanner. They evaluated median travel time overall; morning peak period travel time; and late-night service. This analysis can help identify what routes might possibly achieve our goal of 30-minute travel time by 2030. It also shows what impact traffic congestion has on these travel times.
Some dedicated people looked at how agencies and planners can better identify and access useful data. Their detailed flowchart and guidance should be in every agency’s reference material.
In the end, while data can shed light on problems and can point to the seemingly obvious pragmatic solution, it inevitably comes up against humans, politics, and real world challenges. But bringing people together to solve these challenges with the clarity and insight of data can be incredibly powerful.
We had a fruitful day of vibrant discussions between former strangers from different backgrounds and disciplines. We identified some problems, and begand to identify and work with the materials that might shed light on solutions. Some wished it were a 2-day workshop, because in ways we barely scratched the surface of what’s possible.
There are a ton of resources to bring to our transit challenges. Not just datasets and data tools, but also the very human resources of volunteers who want to help, as well as planners, technologists, and advocates.
We can get more done, and bring new solutions from our different experiences, when we get out of our siloes and work together. We intend to continue the work, and the fun.