it's creating, not predicting, our sustainable future
By Camille Bogan
City and regional planners are complicit in our current transportation crisis; their over-reliance on travel demand models is partially to blame. We’re a year into the extended lockdowns enacted by the COVID-19 pandemic and cities across United States are facing complete shutdowns of their public transit systems, citing plummeting ridership and budget deficits. Millions of Americans are at risk of losing transportation access to essential jobs and daily needs — yet, state Departments of Transportation (DOTs) have continued to move forward with billion-dollar highway widening projects. If we want sustainable transportation infrastructure that reflects the true needs of its users, transportation professionals need to reprioritize their planning methods.
Los Angeles County Metropolitan Transportation Authority’s (LA Metro) I-605 Corridor Improvement Project is an estimated $1.1 billion dollar proposal to widen 15 miles of the 27- mile interstate freeway and surrounding interchanges. A major component of the project proposes widening the Interstate 5 freeway through Downey and Santa Fe Springs, requiring hundreds of homes in Downey to be demolished. After widespread community opposition the Metro board postponed the project in October of 2020 to re-evaluate; however, LA Metro still has multiple freeway-widening projects on deck, as do many other transportation authorities in the United States.
The driving method behind unnecessary highway projects is prioritizing travel demand models--statistical models designed to predict future travel behavior. These models rely on two key assumptions: people make rational decisions about traveling, and that this behavior will continue indefinitely. Our modern political economy was built upon those same assumptions: actors in the market are rational people making the most optimal choices independent of outside constraints. If we look at the continued failure of the market to provide adequate housing or transportation without intervention, it becomes clear that our predictive methods for human behavior are quite limited in their ability to reflect reality.
Metropolitan Planning Organization (MPO) and state DOTs set the priorities for capital transportation infrastructure projects, and these priorities are determined primarily by statistical projections developed by transportation planners and traffic engineers. Projects are then allotted federal funds based on these institutions’ final recommendations for their region. This process is often how we end up with infrastructure that increases highway capacity while neglecting other critical infrastructure, like public transportation or bike networks. Mediocre models simply over-predict vehicular traffic, planning institutions recommend infrastructure to manage vehicular congestion, and unnecessary projects are built and funded.
Many of these projects end up widely criticized by communities and advocates alike for ignoring the proven negative externalities of encouraging vehicular traffic and overbuilding--especially as marginalized communities continue to grapple with the effects of mid-20th century “urban renewal” initiatives. More nuanced techniques such as activity-based modeling have emerged as alternatives to the traditional “four-step model” (trip-based) approach, but ultimately still have many drawbacks. Travel models alone simply cannot capture the full spectrum of constraints individuals might be facing when making travel decisions.
LA Metro’s mass transit planning department recognizes this and in 2019 published its groundbreaking Understanding How Women Travel report. It focuses in on the travel behavior of women—an oft-neglected population of travelers, despite making up 55% of all public transit users in the U.S. This report integrates customer surveys and ethnographies with existing travel data to present a full picture of how women experience the LA Metro transit system.
One critical, but unsurprising, finding is that women are specifically altering their travel behavior—changing usual routes, taking modes they cannot really afford, or avoiding a trip altogether—because of personal safety concerns. One quote from a woman surveyed states, “Sometimes the night options are sparse. So, I’d rather take Uber or Lyft because buses are always late at night and there is no one to call.”
The lived experiences of these women significantly impact the way they make travel decisions, and no travel demand model, no matter how sophisticated, could truly capture these constraints. Instead of trying to account for every possible choice human make, planners and policymakers must stop relying so much on statistical models to tell them how to plan. They owe it to the cities and communities they claim to serve to do their jobs right. If relying on statistical models is allowing for mediocre and inequitable projects to be built, they need to rethink the way they prioritize these models.
Planners cannot claim to be human-focused if their methods are treated at different scales of importance. LA Metro’s report illustrates that a human-focused approach to planning recognizes that an over-reliance on quantitative methods allows for critical motivations behind travel behavior to go unnoticed. This is the direction transportation institutions need to be moving towards—integrating different methods of data collection and treating them with equal importance as statistical models.
There is a wealth of knowledge available to planners from disciplines like anthropology, sociology, and public health on how to effectively integrate and analyze varying scales of data. It will also require funding and coordination from our federal government and planning institutions to commit to this reprioritization. This may seem like an impossible task, but with our current crisis we have no choice--collecting narratives, stories, and feedback from the people who use (or are harmed by) the infrastructure we build is the only way we will truly understand whether what we’re doing is working, or if we need to tear it down and start all over again.