Category Archives: Maps

Getting around in cities all over the world has remained relatively the same over the last half a century: we get picked up by buses or subway cars and, after a few stops and the inevitable delay, we disembark and step onto well-stamped concrete or pavement. The evolution of public transportation -outside of the meteoric rise of information technology- has been a boring, straight line, but the systems themselves, those systems cannot be reduced to linear conclusions. Transit maps, while a feat of utilitarian beauty (and now, interactive dynamism), are views from 30,000 feet where lines are perfectly straight and bank at distinct angles rather than gradual turns. That level of analysis can’t define rider demand at the individual level and demographic mapping from sources like the Census and Immigration Bureaus aren’t granular enough to predict demand sources on an individual scale.

Demand based transit planning has always been a conundrum for the luminaries at transit planning administrations across the country. Transit theorists borrow a term from economics in calling public transit demand “lumpy” because it faces peaks, plateaus, and nadirs on a daily, weekly, and monthly basis. More buses and trains in the mornings and afternoons, less in midday. The lumpy economic theory is good for broad analysis. There are obviously more riders going to and coming from work in cities that have large populations using transit to get to work and most of them arrive around 9 AM and leave around 5 PM. In cities like Boston where a major demographic population is students, the schedules have more flexibility: some students have their first class at 10 or 11 AM —oh, how I miss being a student— and leave their designated zones much earlier or much later than their professional counterparts.  Still, typical demand based model are employed: 9 and 5 are peaks, all other times are not.

A potential 3^rd way for transit planning may exist at the crossroads between internet startups and industrial ingenuity. The two paths represent a divergent means to similar end: provide a consumer a product while altering traditional precepts concerning supplies. The dissolution of boundaries between supply and demand allowed Japan to become a behemoth in automotive and electronics manufacturing and spurred some business savants to base a multi-billion dollar industry off the collective desires of the American public.

Japan’s reinvention of Fordism-era stock rooms, dubbed the “just-in-time” or JIT economy, allowed a land-scarce nation to become the lynchpin of Asian automotive production. Instead of storing parts onsite and paying housing fees for things that may not be needed for days or weeks, the pioneers at Toyota designed a system where parts were delivered only as they were needed. The desired effect is a streamlining —see: less costly— of supply chains and almost non-existent onsite storage costs, a strangely high cost item for auto-manufacturers. This is where we can see the mercurialization of supply but the true abstraction of demand comes from a team of stateside innovators.

The emergence of demand based services like Groupon and Living Social aren’t based on novel economic ideas driving consumers. For consumer demand sites that use a “trigger” to determine when a deal goes on and when it doesn’t —Groupon requires a given amount of people purchase the “groupon” before it is actually offered, hence the playful moniker— the concept is tangentially and, potentially, unconsciously based off the keystone economic theory of market equilibriums. One caveat to this theory is that corrective forces for a glut of demand, which typically include prices increases because when more people want something they’re rationally apt to pay more for it, don’t find a home in these companies; prices for the “groupon” remain flat for the duration of the deal.

What does that have to do with public transportation? Unless you’re riding SEPTA or some of the older systems in Europe and Asia, you now use a card, not a token, to pay your fare. Those cards produce time stamped records of riders and generate volume records that feed into a central database and those receipts are used for the demand models that have been discussed earlier in this essay. What if, instead of those ridership statistics (displayed with graceful practicality in the National Transit Database) going towards long-term demand models they were applied dynamically and geared towards deploying buses and subway cars where they were most needed any time of day. Riders would swipe, tap, or insert their cards and, for the purposes of illustration, the mercury inside a sort of demand thermometer would rise until a train or bus is deployed on its efficient track or route. Instead of subway cars perpetually packed at 5 PM because of linear deployment schedules, there would be a smoothing of the deployment process coupled with real-time ridership numbers.

Would the difference in operating costs run expenses past the burgeoning weight of MTA salaries and benefits? If there was a chance to run fewer trains or buses due to a dynamically produce demand model, would Jay Walder be able to balance his books a little easier?

Broad brushes never end in masterpieces for transportation planning. To say that all we need is a Groupon-based transit-on-demand system is ill-intentioned simplicity and may end up damaging routes that don’t serve very many and at the same time those that need the most. Equality will always be the foil to efficiency and pulls that turn to pushes are inevitable when the only options you have on the table are fare hikes, layoffs, and service cuts. There is opportunity, though, for a dynamic alternative where instead of using swaths of populations as our starting points, we begin the process with a single rider swiping a single card riding a single route. We exist at the center an ever-rising pinnacle of innovation, not just with technology but also in ideas. Transportation planning has not just been a footnote to those advances; it’s been near the center, where it belongs.