Part 1: The Need and the Challenge
For those of us who are committed to addressing climate change, dismantling white supremacy, and reducing economic inequality in the U.S. and who believe that government has a critical role, these have been a difficult few years, or decades. But we hold out hope in the election, at all levels of government, of change-makers with ambitious policy proposals. Clearly, the grassroots organizing in support of these proposals (and officials) needs to continue for them to be adopted, but from my vantage point as a (former) public employee, a similar amount of work is needed to actually implement these policies.
In school, we learned how a bill becomes a law (at least in a sanitized procedural sense). But few curricula cover what comes next: How does a law or a policy become enforceable regulations or take shape as a new program or service?
My sense is that many people perceive government agencies (the bureaucracy, not the politicians) as black boxes. This leads people to assume that every problem is the result of incompetence or lack of political will. In reality, government agencies are a collection of people managing technical systems, infrastructure, and business processes tenuously connected together through years of patchwork and, in all likelihood, under investment. The systems of systems create large amounts of complexity.
Because of the complexity, implementing a policy change or a new program can be just as difficult as getting a decision made. The challenges in making change could come from needing to modify an old technical system, connecting multiple systems together, getting decisions made across the silos within the organization, or unintended consequences that have a ripple effect across multiple systems and processes.
The challenges are often harder in the public sector because services need to be sustainable, scalable, and they should serve everyone, in many cases every day of the year. My former colleagues would tell you that I am obsessed with edge cases, and that is because, unlike the private sector, the role of government is to work for the people on the margins. Working for everyone means figuring out how to make government services accessible in all definitions of the word (people with disabilities, people without internet access, cash users, and on snow days). This requires far more work than aiming to serve the 85th percentile or a chosen targeted market.
Implementation requires people with the skills and commitment to the slow slog of making change deep in the machinery of government. Policy implementation requires creativity and building internal coalitions, and sometimes external partnerships, to find a way. The work usually isn’t particularly visible; and most public sector employees don’t have a public voice. While it is very rewarding to see something you did impacting lives at a large scale, often there is limited public understanding of the immense amount of work to achieve changes and criticism is very easy to find.
If we believe that government can and should solve problems, we need change-makers embedded in all levels of government. So this is my humble call for an expanding squad of public employees ready to laugh and cry their way through the complexity to lasting change.
In this week of rebirth of government in the U.S., I will post some reflections on skills, accountability, and collaboration from my six years of implementing change at the MBTA/MassDOT. For those unfamiliar with my work, I focused on changes to fare policy and programs, and service policy and pilots to increase equity. I welcome feedback on this blog or email at laurelintransit via gmail.com.
Public transit is in crisis, but this moment is an opportunity to rebuild our transit networks to address historic inequities in our transportation infrastructure. When transit ridership dropped significantly across the United States in March, the remaining ridership revealed exact when and where our most dependent riders need to travel. As expected there was significant overlap with the communities of color most impacted by COVID. The uprising for racial justice in the summer of 2020 makes it more imperative that the recovery address past injustices.
Right now public transit agencies in the US are addressing decreased capacity due to social distancing, decrease revenue, and the elevated need to provide access for essential workers. Even with additional federal financial intervention, most agencies will have to make some service cuts and potentially face long term revenue shortfalls. How those cuts are done and how service is restored as riders return will be critical to the equity of public transit and US cities.
COVID could have long-lasting impacts on travel patterns. Higher rates of telework could reduce peak trips. We need changes in how transit service is designed and delivered with a focus on serving all trips, not just peak work trips. Full-time transit riders, especially service workers, need service at different times of day, on the weekends, and to different locations. This makes all day frequency, especially on bus, more important as telework potentially reshapes white collar commuting patterns.
This is a moment with potential for large structural change. Given the scale of the crisis, we have the chance to fix the foundation of public transit networks and to be better equipped to respond to whatever travel pattern changes COVID might bring and to encourage a return to transit. But this will require agencies and traditional transportation advocates to change their usual responses. The conversation shouldn’t be about whether or not to cut service, instead it should focus on how to best serve the needs of the moment and the future. Some service might need to be scaled back due to lack of demand and in order to increase resources elsewhere in the network.
A small peek inside my brain
The normal goal of a commute trip is to minimize total time. But if you are bicyclist you are also concerned with conserving energy or retaining momentum. Since traffic signals are not coordinated for bicycle speeds (or at all) I have developed a system of adjusting my riding speed between each signal to minimize time stopped.
What I call the bicycle game started with counting the number of times I put my feet down, but evolved into a serious data collection effort. Everyday I record the total trip time, riding time, average riding speed, start time, and distance.
In my attempt to optimize my commute I consider three main indicator variables:
– total time,
– percent of total time stopped, and
– average riding kph.
I also compare three riding strategies:
– baseline (just riding)
– timing (trying to not stop), and
– speed (biking as fast as possible)
The following graphs compare each of the indicators for each strategy.
Percent of Time Stopped compared to Total Time
Average Kilometers per Hour compared to Total Time
Average Kilometers per Hour compared to Percent Time Stopped
While more data is needed, it is clear that while the timing game has produced the best individual results (shortest time and an almost zero stopped time commute) it has a large variance. So either I am not that good at it or there are too many factors out of my control (start time doesn’t seem to have a noticeable impact). Riding fast is the most reliable method to minimize total time, but it results in a lot of wasted energy. Some combination of speed and timing is the most optimal solution, but it might be almost impossible to reliably replicate due to all of the outside factors.