This post is guest-written by Chao Liu, Hiro Iseki, and Gerrit Knaap, researchers from University of Maryland’s National Center for Smart Growth, who helped Metro develop our Land Use Ridership Model.
Even though Metro doesn’t control where new jobs and households locate in the region, these decisions are critical to the agency’s ridership and financial future.
It is well known that the form and intensity of development in and near rail transit station areas can have measurable impacts on transit ridership. For these reasons, transit oriented developments (TOD) generally feature high-density construction, mixed land uses, and bike and pedestrian friendly infrastructure. But not all TODs are alike, and the effects of TOD on transit ridership are likely to depend on how well the station is connected both locally and regionally, whether the station is near the center or end of a transit corridor, and what kinds of jobs and household are located nearby.
To explore how different forms of development might impact ridership on the Washington Metrorail system, Dr. Hiroyuki Iseki and Dr. Chao Liu assisted Metro to develop a direct ridership model (DRM), called Metro’s Land Use Ridership Model. A DRM uses statistical techniques to quantify the relationship between entries and exits at rail stations and land uses nearby. This model can then be used to estimate the number of passengers who will access the station, by waking or biking, as a result of changes in land use features, transit service characteristics, and socio-demographics within the walkshed of any given station.
The direct ridership model includes a large number of variables for each station, including the density, diversity, and design of local environment; transit service and connectivity; job accessibility by auto and transit; walk score; the availability of parking; the demographics of nearby residents; the number and types of jobs nearby, and more. The model was estimated for the AM Peak, Midday, PM Peak, and Evening travel periods. The AM Peak model is best suited for estimating the increase in morning boardings that would result from locating more households near the station; the PM Peak model is best suited for estimating the increase in afternoon boardings that would result from locating more jobs near the station.
Map 1. Predicted AM Peak Entries per New Household
The impact of adding jobs and households near stations varies by station area. Map 1 above, for example, shows the estimated entries per new household in the morning peak—that is, how many additional boardings would occur in the AM peak if one additional household was located in the walkshed of the station. Stations shown by red dots gain more than 0.57 boardings per day, for each new household in the walk shed, while stations shown with green dots gain only about 0.20 boardings per day. As a concrete example, Rhode Island Row is a 274-unit, mixed-use, TOD project built on a WMATA site. Situated along the busy Red Line, the project has long been considered as a prime location for new housing development. According to the DRM model, adding 274 new households near the Rhode Island station would increase boardings by 144 passengers in the AM peak. The same development at the New Carrollton station, however, would have added only 52 passengers. This is because, compared to New Carrollton, the Rhode Island Avenue station has better job accessibility and more frequent transit service, and is thus likely to stimulate more transit ridership. Read more…
