The pandemic might have changed some things, but I think mostly it revealed or exacerbated existing conditions. So far it has not fundamentally changed my view of the future of transportation. Three key realities remain true. One, we have to reduce emissions from transportation to address climate change and air quality. Two, we have a limited amount of public space for mobility and increasing demand for it. (The pandemic intensified the demand with more deliveries and public outdoor space for dining, recreation, and non-motorized transport.) Three, our transportation system is unequal, unsafe and inefficient in both funding and how public space is allocated and enforced. (This past year further illuminated the inequity and violence around enforcement in public space and expanded my definition of safety.)
Maybe because I was a math major in college, when faced with multiple problems I like to find the intersection of their solution sets. In this case, the use, space allocation, and funding for systems of shared transport is clearly in the intersection of all three problems. While the space and emissions benefits of shared transport are fairly clear, shared transport is also important as a place for social integration. I believe it is critical for a multiracial democracy to have places where people safely share space with people from different backgrounds.
Over the past decade my thinking about shared transport expanded. In part because I spent several years living and traveling in the Global South and saw a variety of shared transport systems that have been around for a very long time. And as new technology (e.g. electric scooters) and the ability to book fares on smartphones has created new shared mobility opportunities (and a new place for competition to take place).
As I left my research role in Santiago, Chile I wrote a paper about shifting regulatory frameworks for transportation (presented at Transportation Research Board 2017). My premise is that transport can be framed on two axes: the spectrum of how collective/shared the vehicles are, and the role of the state in providing the service (publicness). This graphic could be updated, but the idea is still useful.
As the graphic shows, shared transport ranges from bicycle sharing to trains that can carry thousands. We need many types of shared mobility to match different land uses, demand levels, and personal preferences. There is no one size fits all regardless of who is operating the service. (I want to start thinking about how urban freight/deliveries fit in.)
Given the intersection of problems we need to shift trips from private motorized vehicles to shared vehicles (and non-motorized modes). The important policy questions are often around what is the role of the state in regulating, funding, and operating each service to achieve this goal and provide equitable access. The graphic illustrates there is an increase in publicness as sharing capacity increases. This is due to the need for large capital investment that lends itself to a public monopoly, but public ownership exists across the sharing spectrum.
I don’t know exactly what the mix of public and privately operated shared transport services will be in the future (or how Autonomous Vehicles will manifest), but regardless of that future it will be essential to have a digital platform that provides users with information about costs, in both time and money for any given trip, and books fares. Many tech companies have figured this out and are trying to be the platform. But it is critical that the platform be owned by the public sector.
Public control is necessary to ensure fair competition, facilitate equitable access, and achieve public policy goals. The digital platform is essentially the marketplace for shared transportation and, especially if there are private operators, the site of competition by giving consumers (comparable) information. The public sector can set the rules for access to the platform, like ADA accessible vehicles or providing service in low-income communities.
A digital information and ticketing platform also provides the mechanism for government subsidy for transportation, either for equity goals or incentives to shift behavior to shared trips. Subsidy could be applied at the trip level, for types of services, or for individuals. Even if some public transit service is free, a platform allows public subsidy for low-income people to make trips where and when high capacity public transit service doesn’t make sense. For example, free transfers to bike sharing controlled by a different entity or a subsidized taxi trip late at night.
Another key reason for public ownership of the platform is to ensure access for cash users as the trend toward smartphone and contactless payments continue. Cash use is needed for under-banked people and privacy reasons. The platform has to be attached to an easy way for people to add cash to accounts that can be used to pay for all forms of transportation.
The MBTA Fare Transformation project is designed to be the foundation of a public platform. After integrating all of the MBTA services together, the plan is to bring in other services and develop joint fare products. The retail and fare vending machine network will provide access to cash users not only to the MBTA, but potentially to other shared mobility options. If all goes according to plan, it is a good example of a public agency acting proactively to protect the public good in the future.
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.