“I have
travelled the length and breadth of this country and talked with the best
people, and I can assure you that data processing is a fad that won’t last out
the year.”
-- Editor,
Prentiss Hall Books, 1957
Last week,
the Globe profiled
an effort by 23-year-old entrepreneur Matthew George to use data analytics to provide “pop-up”
bus service across many underserved routes in the Cambridge-Boston area. This “pop-up”
service—called Bridj—is designed to use
data about “where people live, work, and play” to predict where non-stop
service is needed and adjust schedules based on time of day/day of week, etc.
George’s introduction of disruptive analytics to the
metro-Boston transit network is long overdue and I’m anxious to see how his
system works (and to try it myself come July 4th weekend). But, as noted by MIT
Professor Nigel Wilson, George’s service
(which is expected to launch at a cost of $5-8 pre trip) has the potential to
siphon riders from the MBTA. Indeed, while the Bridj homepage champions “Better
Transit. For All,” it is not yet clear whether the business model can rely
solely on routes not directly served by the T.
In a normal setting, competition would be an
unquestionable good—with the better product/price/service winning out over
time. However, public transit is a unique animal—a deeply subsidized public
good that must cater to the needs of very low-income city dwellers (among
others).
To
his credit, George seems quite cognizant of this fact and has indicated that he
hopes to reduce fares to a price approaching a single-ride T-pass ($2-2.50). However, it is ultimately not the job of
entrepreneurs like George to worry about how their innovations might affect
competitors like the MBTA.
Instead,
as I briefly noted
last year, what the MBTA and other
transit agencies from New York City’s MTA to the smallest regional network in
Berkshire County need to do, is to get in the data analytics game themselves.
In Boston, this effort should include investing in smaller vans that can
operate at lower cost than articulated buses, depending on demand, GPS tracking
to allow riders to plan their trips, and demand-responsive
transport during late nights and weekends. In the spirit of George’s “pop-up”
service, demand responsive transport covers a fixed service area but without
fixed routes, allowing it to cater to fluctuations in ridership.
This
type of planning should not be limited to buses, but should instead be used to
integrate a municipal transit network’s bicycles as well. In NYC, CitiBike
recently released a trove of data
charting hundreds of thousands of rides and, as shown in the graph below from
the NYU Rudin Center for Transportation
Policy and Management, there is a slight, but
meaningful correlation
between subway disruptions and use of CitiBike along those routes.
Dubbed “reactionary biking” by the Rudin Center, this pattern
should lead to partnerships between the MTA and CitiBike. For instance,
when there is a planned service outage—especially a long-term outage, like the 5-week
closure of the G train’s Greenpoint Tube planned for this summer—MTA should not
only provide replacement bus service, but also work with CitiBike to extend
bike share to affected communities. Similarly, the two systems should share
data on ridership so that CitiBike can do a better job of balancing stations
near transit hubs which, at certain times of the day, are overrun with
passengers (most notably on the Lexington Line (456)).
In 2012,
Peter Sondergaard of the Gartner Group declared, “Information is
the oil of the 21st century, and analytics is the combustion engine.”
If
Sondergaard is right, public transit
systems cannot sit back in the horse and buggy age while private companies like
Bridj act like the Maseratis of the data world. They need to get in the
game themselves and use “big data” to increase efficiency and improve service
for the millions of Americans who rely on buses, trains, trams, and bike share.
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