How do you get Angelenos--some of the most car-focused people in the world--to use mass transit?
The challenge came from a consortium of L.A. design and transit types (namely Southern California Institute of Architecture [SCI-Arc], The Architect’s Newspaper and LA Metro in an open invitation for ideas that would increase public transportation use in L.A.’s car-centric landscape. In that “landscape,” each auto produces an average of 1.5 pounds of carbon dioxide emissions per mile per person, which compares unfavorably with public transit. Mass transit riders produce on average just 0.18 pounds of carbon dioxide per mile per person.
As architects who also teach, we were intensely interested in the possibilities inherent in this challenge, which was initially waged several years ago. The interplay of design and psychology (not to mention a multitude of preconceptions) that any legitimate answer would have to address fascinated us.
In lieu of a physical solution--which might have been expected from us as architects and included a better bus stop, higher capacity buses, even better advertising--we offered a virtual solution; we devised a more responsive, more personalized mass transit system whose essence is the growing data cache that municipalities, transit authorities, and cellular network providers have at their disposal.
Mounds of data (a.k.a. big data) is being collected “out there” and could be intensely valuable if it were keenly networked—mined, combined, compared, and ultimately, licensed. That’s the crux of our idea: leveraging existing data streams to optimize the public transit system in L.A. (or any city, for that matter). In doing so, the system could be more responsive, more accurate and personal, thereby reasserting the individualist mentality that has powered the mythology of Los Angeles for generations.
We should note our software proposal--an app--wasn’t based on approximation or predictive algorithms. Instead, it relies on the real deal: real-time data generated by cities and individuals. We should also note that what started as an entry in an open competition to urge Angelenos to ride mass transit has turned into a multiyear project with us considering the broader ramifications that mined and “packaged” BIG data could have on cities.
Still, the initial concept, which we call NETWORK_LA Transit, remains a legitimate (although still speculative) response to the original challenge put forth by SCI-Arc, etc., for two important reasons. It does not require major infrastructure investment, which disrupts city life and runs the risk of being obsolete by the time it’s built. And it does not require that LA Metro purchase more public transit vehicles.
One of the most controversial parts of the idea is about getting people to rethink the concept of fixed routes. A clear evolution of the rail-as-guidance limitation of the streetcar and railroad, this legacy constraint is quickly antiquated in a world of user-generated traffic and drive-by-turn GPS instructions. Furthermore, the notion that trains and buses are the totality of any city’s mass transit fleet neglects the potential of incorporating pay-per-use automobiles or shared, hyper-flexible personal vehicle types such as bicycles and scooters. Evolving the transit paradigm, especially in a city where public transit is disconnected and expected to cover a vast footprint, is crucial in a world where customization is increasingly ubiquitous and where heightened levels of sustainability are being legislated into public policy.
What NETWORK_LA Transit proposes is an integrated set of ideas. The concept centers on the belief that shared intelligence can elevate the commuter experience while transforming L.A.’s labyrinthine public transit system into an on-demand service that performs optimally for individual riders and, ultimately, the larger Los Angeles metropolitan region.
Here’s how it works. Here’s the opportunity for cities:
Shared/rental bicycles, scooters, and cars as well as personal rapid transit would be added to the current public fleet of buses and trains through strategic public/private partnerships. This varied fleet of alternative vehicles would deliver a higher level of efficiency in terms of distance covered, cost per commute, and carbon dioxide levels. A train rider, for instance, would be able to hop on a rented bike or scooter for the last two miles of his commute (if he were so inclined), saving him the time of waiting for a bus and saving the environment 0.18 pounds of carbon dioxide emissions per mile.
The location of ground transportation, stops, and users would be connected in real time, using GPS. And then, to complete the triangulation, a GPS-enabled app—what we’re calling tripFinder—would automatically scan the network and provide riders with the best, real-time trip itinerary while optimizing the current status of the public fleet. TripFinder might inform the rider of the most efficient route using the various vehicle types in the fleet and reserve certain vehicles in advance to provide a grab-and-go experience and a level of customization that does not currently exist.
This is, perhaps, the part of the concept that is most foreign to mass transit and often attracts the most attention from naysayers. Existing bus stops would remain intact, but the routes that connect them would be “liberated” so that buses could freely move among the fixed points based on real-world driving conditions. This increased flexibility (appropriate for express routes and longer distances) would enable buses to respond more immediately to user demand.
For example, in L.A., a commuter’s bus ride from Santa Monica to Downtown L.A. could involve a different route every time, from the last local pickup to the first downtown drop-off, in order to optimize ridership loads while providing that commuter the fastest trip each time. This also would allow LA Metro to better balance the geographical coverage of its vehicles. Buses could be redeployed from areas with light ridership to those that are busier in real time.
Taxpayers would not shoulder the cost of the expanded LA Metro fleet. Instead, LA Metro would fill the transportation voids by licensing its data streams to ground transport companies that provide the alternative vehicles (rental bikes, scooters, cars, personal rapid transit). These licenses would grant access to tripFinder’s real-time information, thereby co-opting these private entities into the public transit system while providing an additional profit center for LA Metro.
As a footnote, this virtual infrastructure also could leverage other relevant travel information for consumer use, thereby increasing the value of—and potentially the income from—these licenses. For instance, by providing a driver with real-time traffic information such as the timing of stoplights, commuters may choose to adjust their driving and perhaps increase the fuel efficiency of that individual vehicle. Thus, creating a transit feedback loop--that’s only possible with cloud-based computing--is established, in which private user needs combined with known schedules and geo location accurately augment the publicly provided transit information
The result of our original concept--NETWORK _LA Transit--is certainly bigger than L.A. It’s mass customization for mass transit: a user-influenced, on-demand system that responds to the needs of individuals rather than forcing them through an odyssey of fixed routes and disconnected systems. More than that, it’s a concept that raises the question: “What might cities do with all their BIG data that currently sits unconnected and underused?”