Light rail is an attractive idea, especially in famously congested Los Angeles. Why sit stuck in traffic while the light rail glides by?
Light rail projects are booming around the United States. Reports from the National Transit Database show that between 1991 and 2012, light rail transit capacity increased from 27 million to 99 million service miles nationally. Light rail service, in fact, has grown at a higher rate than bus, subway, and other public transit modes. Los Angeles is part of this trend. LA Metro has the most ambitious urban rail transit development program in the U.S.: Projects worth approximately $8 billion are currently under construction. The first segment of the Los Angeles Expo Line, between Culver City and Downtown LA, opened in 2012 as part of this widespread recent investment.
One of the common justifications for investing in light rail is its potential to reduce roadway traffic congestion. Yet, little evidence exists to support this claim. There are many studies of the impacts of light rail, but few have examined its impacts specifically on traffic congestion.
We took advantage of a unique data set to analyze how the Expo Line affected transit ridership and road traffic in the corridor — and found that the project has had a positive impact on the former, but not much effect on the latter. Our results indicate that the real benefits of rail transit investments are not in traffic reduction, but rather in increasing the accessibility and popularity of transit within high-demand corridors.
We identified three conditions that must be satisfied for a light rail system to decrease corridor-level traffic congestion:
- A net increase in transit service and accessibility, relative to previous transit services within the service corridor, in order to attract new passengers rather than those who already used transit. The increase must be large enough to be perceived by individual travelers.
- Potential demand for transit travel within the corridor must be enough to generate more passengers. Specifically, enough existing travelers must be willing to shift to transit and new travelers must be willing to choose transit over other modes if quality transit service such as light rail is introduced within the corridor.
- The new transit system should not interfere with or slow down roadway traffic within the corridor.
If these conditions are met, light rail systems can attract new riders by promoting car-to-transit shifts, and thereby reduce congestion, improve mobility and reliability of travel, and increase person throughput across their service corridors.
“A natural experiment”
The first operating phase of the Expo Line connected Downtown Los Angeles with Culver City (Figure 1), running east-west for nearly nine miles through a dense and congested part of the city. The line is roughly parallel to the I-10 freeway, and both the corridor and several parallel arterials have extensive bus transit service: Metro’s local and rapid buses, Culver CityBus, and Santa Monica’s Big Blue Bus routes. As the corridor suffers from heavy peak-period traffic, Metro marketed the Expo Line as a means to increase transit mode share along I-10 between downtown and the Westside, noting that it would provide Angelenos “real options for parking their cars, hopping on the bus or train and beating high gas prices.”
Figure 1. Expo Line Phase 1 alignment
We wanted to answer two questions about the Expo Line: Did it significantly increase transit ridership within the I-10 corridor? And did it reduce traffic congestion and improve travel time reliability along the I-10 freeway and nearby parallel arterials during weekday peaks?
The Expo Line addition resulted in a small increase in transit service supply within its corridor — about 4 percent more vehicle hours of service. A subsequent net increase in transit ridership along this service increase is a necessary but not sufficient condition for any measurable impact on traffic. Even if transit ridership increased, we needed to determine whether the increase was enough to affect traffic performance, which depends on the magnitude of transit service increase and where the new passengers come from. If new passengers are mainly previous car users, this could signal more of a potential traffic benefit than if they were previously using other transit routes or modes, biking, walking, or not traveling at all.
So what happened after the line opened? Expo saw around 20,000 average daily boardings in the three-month period immediately after the opening of the line. Comparatively, the annual average daily traffic on the I-10 freeway within the corridor is about 300,000 vehicles.
Given such a difference in scale, we did not expect dramatic shifts in traffic as a result of the Expo Line. We knew that measuring small changes in traffic performance and attempting to attribute those changes to the Expo Line would be challenging. All the other changes taking place in the corridor, such as traffic signal timing or fluctuations in fuel price, would affect the measurements, too.
Challenges aside, the Expo Line opening was a “natural experiment,” giving us the opportunity to evaluate corridor changes. In order to isolate the new line’s effects, we used a research design that compared transit use and traffic system performance in the corridor before and after the line’s opening, relative to changes in a control corridor. For our control, we identified two similar locations not affected by the opening and performed the same before/after comparison (Figure 2). Our data covered two three-month periods, one before the opening (November 2011 to January 2012) and one after (November 2012 to January 2013). Expo Line service began in June 2012.
We selected control corridors that were comparable to the experimental corridor in terms of baseline conditions as well as in changes to transit demand and traffic system performance. We used three different measures of traffic system performance:
- Average speed, which indicates level of congestion
- Standard deviation of speed, an indicator of day to day variation
- Average buffer time, which, according to the Federal Highway Administration, “represents the extra time (or time cushion) that travelers must add to their average travel time when planning trips to ensure on-time arrival.”
Figure 2. The experimental and control corridors
How we assembled our Big Data
In 2010, our research team at the USC METRANS Transportation Center partnered with LA Metro to develop the Archived Data Management System (ADMS), a massive volume of geocoded and time-stamped streaming data from a variety of highway and transit sources. The system allows researchers to conduct detailed studies that were previously either impossible or extremely costly to perform. Examples include the impacts of new transportation investments such as subway line construction, policy shifts such as fare increases, and exogenous shocks such as gas price changes.
ADMS served as the principal data source for this study. Across the traffic corridors, 74 freeway and 1,066 arterial sensors provided traffic performance data on speed, volume, and occupancy roughly every 30 seconds. These were aggregated into 15-minute averages to smooth out the random fluctuations in traffic patterns while still capturing short-term changes in performance.
The final set included more than 816,000 freeway data points and more than 15 million arterial data points, across both the before and after periods. The transit data from LA Metro included configurations of bus and rail routes, the locations and boarding/alighting counts of stops and stations, and planned service schedules. We additionally accounted for transit network and schedule changes that are typically implemented in June and December.