Downtown L.A. Rendering — Los Angeles, California
A model of a city.

Does Los Angeles have a public transit future?

Research Project Name

Network L.A. Transit

What We Did

In response to a competition calling for ideas on how to best increase public transit ridership in Los Angeles, we asked ourselves why L.A. citizens are drawn to the car as a dominant mode of transportation and why they choose not to use public transportation. To answer these questions, we investigated several areas, including economic, cultural, ecological, conceptual, and legislative factors that explain why Los Angeles citizens consistently choose their cars over alternative modes of transportation.

The Context

L.A. is the oldest postwar American city, and thus built its transportation identity around the automobile as an extension of individual manifest destiny. As a result, Los Angeles is dominated by car travel. The average commuting distance to and from work for an L.A. citizen is over 30 miles and 79% of those trips are by car alone. Cars pose an increasing environmental problem: A car produces more than seven times the CO2 emissions per person that a bus does.

Los Angeles is also a city committed to change. It will be spending over $40 billion in the next 20 years to improve transit. The Metro Rail system has already become an economic revitalization generator for Hollywood and Downtown, and will continue to be so with increased ridership. The growing density is also creating new opportunity. The population of the Los Angeles metropolitan area has increased more than 32% in the past 12 years and ranks #1 in the US for the highest density of people, at 7,000 people per square mile.

The Results

Increasing the movement of people, not cars, should be the goal of any public transit initiative. For this project, we proposed an integrated set of ideas based on user needs and aimed at adapting the current system to improve its performance at various scales. The belief is that a more responsive system and an improved user experience would ultimately pave the way to meet Los Angeles’s transit challenge. The result is a user-driven, on-demand software solution that would meet the needs of each rider, allowing the network to organically adapt to shifting ridership and improve overall service. Los Angeles, as a city of multiple centers whose relationships are constantly changing, could have transit routes that adapt to the needs of its passengers rather than forcing passengers to use multiple fixed routes. This software solution is also an opportunity to avoid larger-scale investments, which disrupt city life and run the risk of being obsolete by the time they are complete.

What This Means

Increase vehicle choices. Including alternative modes of transportation in the L.A. Metro system can provide higher travel efficiency and address the various scales and distances that transportation covers.

Enable bus flexibility. Keep existing transit and bus stops but liberate the routes that connect them. Buses that respond immediately to user demand can create a dynamic system that is highly efficient and personal.

Leverage existing data. Overlap the location of all ground transport, stops, and users through GPS to coordinate their relative positions, needs, and capacity in real time. GPS-enabled applications can automatically scan the network to provide users with the optimum trip itinerary while also making the public transit fleet more efficient.

Expand the network. Fill in transportation voids by granting access to real-time information through the selling of licenses to alternative ground transport entities, creating a market for these additional ground transportation businesses and providing a new profit center for the L.A. Metro.

What’s Next?

Mounds of data (a.k.a. big data) are being collected and could be intensely valuable if keenly networked, mined, combined, compared, and ultimately, licensed. That’s the crux of our idea: Leverage 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 thereby more personal. More research needs to be done to determine how to optimize big data for the benefit of the public.

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Li Wen, Shawn Gehle, Rob Jernigan, Richard Hammond, Hae-Sun Kim, Tam Tran, Alex Webb

Year Completed