Posts Tagged ‘revenue’

Metrorail Revenue by Station – Visualized!

April 15th, 2015 6 comments

Where and when does Metrorail generate the most farebox revenue? So far the data reinforces the notion that ours is a truly regional system with strong revenue contributions from all jurisdictions – but of course, the story is far more complicated than that…

What kind of rail system is Metrorail? Urban subway? Commuter rail? Hybrid? The answer of course is all of the above. And if that is the case, what kind of ridership and revenue patterns should its stations and system exhibit? High levels of peak revenues with heavy commuter lot usage but relative inactivity during the day? Lower levels of peak period activity but a steady stream of usage throughout the day? Depending on your perspective (and travel patterns) one might argue for either, and it might seem easy to apply a blanket classification to Metrorail and declare that “only urban stations cover their cost” or “commuter stations contribute largely to Metrorail’s revenue picture.”

Well, when you throw the data up on a map, it becomes clear that there are no easy answers, and no one right way to view the revenue picture of our tri-jurisdictional hybrid rail network. Some conclusions from the data are intuitive, some less so. Among them:

  • Differences in ridership across stations are bigger than differences in revenue, so ridership is a stronger explanation of differences in revenue than fares. For example, Shady Grove’s average fare in the AM Peak is $5, which is twice as much as the smallest average fare. On the other hand, ridership at Shady Grove is ten times higher than other stations, so the ridership better explains the station’s revenue.
  • In the AM Peak, the terminal stations dominate in terms of revenue contribution. Union Station functions as an internal “terminal station,” meaning that the commuter rail and Amtrak connections to Metro are extremely important to the overall ridership and revenue picture.
  • Other stations with strong bus or commuter park-and-ride infrastructures also pop in the AM Peak, such as Silver Spring and Grosvenor.
  • Note how well the non-Silver line stations in Virginia perform in the AM Peak, as well as the somewhat expected better performance of the Shady Grove branch of the Red Line in the AM Peak.
  • In the PM Peak, the core is king. Stations like Farragut West and North, Metro Center, L’Enfant Plaza are producing $50,000 apiece every evening thanks to their job densities, reinforcing the importance of improving their capacity for the future in Metro 2025, as well as their huge importance to revenue today. By comparison, in the AM Peak, only Shady Grove and Vienna approach these levels of revenue at roughly $40,000 per station.
  • The New Carrollton and Largo Town Center branches of the Blue/Orange/Silver Lines contribute significantly less revenue than other branches, and this directly relates to the relative lack of transit-oriented development along these spines.  The station areas on these lines enjoy a superb level of rail connectivity to the region’s primary job cores, but without sound transit-oriented investments to-date, they have not yielded the type of ridership and revenue commensurate with the capital investment. Imagine what Metro’s revenues (and farebox recovery) could look like if these segments were properly developed!

We’ve been examining the data ourselves as we continue forward with Momentum’s call for us to ensure financial stability for the Authority and have created the visualization for you to play with. We’d love to know what you see!

Going Up – Why the Construction Pipeline Means Higher Metrorail Ridership (Part One)

April 6th, 2015 5 comments

We’ve claimed that Transit-Oriented Development (TOD) projects in this region will be critical to Metrorail ridership and sustainability. The good news is that our assertions are grounded in statistically rigorous evaluations of TOD’s impact on Metrorail ridership – here’s how. (Part one of a two-part series).

While factors like fares, service, and the economy can certainly explain some changes in Metrorail ridership, one absolutely fundamental explanation of differences in walk ridership between stations is development.  Why does a station like Landover see only 50 riders arrive on foot each morning, and a station like Crystal City see over 3,000?  Why does a station like Bethesda see balanced ridership in all directions, where a station like Suitland is almost entirely one-direction? Development. Even a simple scatter plot shows that households alone near the station explain 70% of AM Peak walk ridership!

Planning studies have long-posited that transit-oriented development is such a key part of driving ridership, and if that is the case, then TOD is vitally important to Metro’s long-term financial sustainability.  We at Metro needed to quantify this link in a more sophisticated and system-specific way, and so we created a way to calculate the impact of land use changes (household growth, employment growth, new development) on ridership and revenue.

What is a Land Use-Ridership Model? To help, Metro’s Planning Office has built a Land Use-Ridership Model that will predict changes in Metrorail ridership as a result of occupancy changes (growth, decline, new development, etc.) in the station area.  This model helps us get very specific when it comes to modeling the impact of land use changes on ridership and revenue.  It helps us answer questions such as: “When developers build a new apartment building next to a Metrorail station how much ridership and revenue will Metro realize?”, and; “If an office building is proposed at one of four Metrorail stations, which location maximizes ridership and revenue without exacerbating core capacity constraints?”

LURM general flow

This tool is based on a rigorous understanding of the link between land use and the rail ridership we see today and is built on “direct ridership modeling techniques” found in academia.  It also focuses specifically on “walk ridership” (which constitutes 38% and 78% of our AM and PM peak ridership), since rides related to bus transfers, parking, and other access modes are less related to adjacent land uses.

To build this, we analyzed the actual quantity of walkable land uses from each station area, assembled detailed information about land uses and densities in those areas (households, jobs by industry type), and also controlled for other, non-land-use factors that shape ridership – like network accessibility. In all we worked through over 200 independent variables in our modeling and also brought in experts from the University of Maryland’s Center for Smart Growth, professors Hiroyuki Iseki, Ph.D. and Chao Liu, Ph.D., to bring their analytical and statistical firepower to the fray.

How We Built It. We defined the walkable area as a half-mile walk along a road network, so we account for barriers like highways and fences.  The half-mile cutoff is a bit longer than the median actual walk distance reported by our riders in the 2012 Metrorail Passenger Survey. For each station and its walk shed, we tested the following kinds of factors: Read more…