Gaining access to a workforce that is increasingly drawn to urban, transit-oriented locations has become a critically important consideration for employers deciding where to locate their offices. Here in the Washington, DC area, we are seeing this trend firsthand. In March, the Washington Post reported that one of area’s major employers, Marriott International, intends to relocate roughly 2,000 employees from its current headquarters in suburban Bethesda. In the article, Marriott’s corporate leadership directly addressed the issue of transit accessibility:
“[Marriott CEO Arne M. Sorenson] praised the area’s ‘supremely well-educated workforce’ but said in order to attract the best talent he needed a location that would appeal to young workers.
‘I think it’s essential we be accessible to Metro and that limits the options. I think as with many other things our younger folks are more inclined to be Metro-accessible and more urban. That doesn’t necessarily mean we will move to downtown Washington, but we will move someplace.’”
Taking a look at the transport accessibility for Marriott’s current site you can quickly see why this matters. At present Marriott’s transit “travelshed” places central DC at nearly 60 minutes for a typical weekday morning commute. Other parts of the region with substantial workforce, including nearly all of northern Virginia, are well beyond 60 minutes by transit.
Marriott’s choice of location makes a big difference in which part of the region’s workforce they can engage, particularly if those current or future employees demand a car-free commute. And the evidence increasingly shows that younger members of workforce value choice in their commute.
Marriott’s reexamination of their location is hardly unique. In Washington and in urban areas across the country, more and more companies are reassessing their corporate location decisions in light of the trend toward more walkable, transit-accessible workplaces. What’s more, we are beginning to see empirical evidence of a direct relationship between accessibility and worker retention, particularly access by transit and other alternatives to the private car. Last month, Citylab reported on the results of a study finding that “counties with transit systems have lower turnover rates.”
Clearly, companies have identified the challenge before them. What’s needed now are more effective tools to help employers measure the accessibility of workplaces – both existing sites and potential future ones.
This is where recent developments in open data and open transportation technology come into play. By combining available open data – for instance, OpenStreetMap and GTFS data describing the transportation network and the U.S. Census’ Longitudinal Employer-Household Dynamics (LEHD) data describing employment demographics – with multimodal network analysis tools, we can begin to answer questions about optimal workplace location in a compelling way.
Over the past two years, Conveyal has been building a suite of open source outreach and analysis tools, including Transport Analyst, a multimodal accessibility analysis tool, and Modeify, a platform for personalized transportation demand management (TDM) intervention. We are now developing tools at the intersection of these two applications – accessibility analysis and TDM outreach – in the context of supporting workplace location decisions.
Consider the case of the Marriott relocation in Washington. Marriott has already identified that it wants to move to a place with good transit accessibility – and specifically, good accessibility for its target employee base. So, which areas should Marriott focus on? A traditional approach to answering this question might look simply at the density of transit coverage at various locations, which will invariably lead to places like downtown Washington. But what we’re really interested in is accessibility specific to this employer – e.g. how many existing (or potential) Marriott employees can actually reach a site using current transit service? And how does this compare to alternatives?
The new tools and data described above allow just this sort of analysis. First, we can create an approximation of the existing Marriott workforce using LEHD data, which gives us block-level employee characteristics as well as origin-destination commute patterns through the LODES dataset. By combining these known worker flows with a comprehensive model in Transport Analyst, we can quickly see which areas of the Washington region offer the best accessibility to the current Marriott workforce via different commute options (in this case, a walk-to-transit commute of 60 minutes or less):
This graphic shows the number of current employees that can access a potential new headquarters location within an hour transit commute. Each block is colored by the number of current employees that can make this commute in 60 minutes. In the darker areas up to 40 percent of the current workforce could make the trip by transit. In their current location only about 10 percent can travel by transit today.
Not surprisingly, the overall access shed is weighted toward the north side of the region, where many of the current employees live. In particular, we see concentrations of accessibility around the major transit hubs in Montgomery County, such as downtown Bethesda and Silver Spring, as well as downtown DC and Dupont Circle. If Marriott is looking for locations that provide optimal transit access to the current employee base, these would all be good choices.
We can take this step further, however: rather than limiting our analysis to the existing employee population, let’s consider a larger population of potential employees. After all, part of the motivation behind corporate relocations is to make a company more competitive within the broader talent pool. Because LODES data is broken down by employment sector, we can create a similar graphic for all workers in the region that fall into a specific category of interest to the employer (in this case, corporate management):
Here, we see not just current employees traveling to a potential new Marriott headquarters location, but all employees in the region that fall into certain categories of particular interest – managerial and professional employees. This is a larger and more regional slice of the workforce, and as a result we begin to see areas of high accessibility emerge that would not have been identified if we only focused on current employees – for instance, the Rosslyn and Pentagon City station areas in Arlington.
The above analysis is only the beginning of what is possible. Once the company is ready to begin evaluating specific candidate sites, we can use the accessibility models to translate these decisions into real-world measures – the typical change in employee commute time, for example, or how much the company saves by not having to provide parking for much of its workforce. Furthermore, if given access to a database of a employee locations rather than census data, we can provide personalized results for each individual.
Tools like Transport Analyst and Modeify provide a powerful platform for answering the critical questions employers face when when making location decisions. For more information, contact us at firstname.lastname@example.org.