Cities must make investments that consider the life and longevity of any major infrastructure investment, accounting for anticipated future growth and development. Such projections should reflect a city’s adopted goals and project an intended outcome, coordinated with land use controls.
The design year assigned to a roadway represents an estimation of the future traffic demand and volume expected on that facility.
Design year typically relies on travel demand models and other methods that often implicitly assume steady traffic growth. These projections tend to stand at odds with both local policy and recent travel trends. While travel demand modeling has evolved into a highly sophisticated and refined field, it still remains an educated estimate and should be qualified by intended outcomes and goal-driven city policies.
A 2% compound traffic growth rate doubles traffic in 35 years.
Driving per capita continues to decline, even as gas prices have stabilized and the economy has shown signs of recovery.
While trends indicate that traffic volumes have leveled off or even decreased over the past 10 years in jurisdictions throughout the United States, traditional forecasting substantially overestimates the potential for traffic growth.2
Similarly, many modeling efforts underestimate the potential benefits (traffic reduction impacts) of improved land use decisions, natural growth in other modes (such as bicycling) and an overall cultural shift in urban mobility choices.
The graphic below illustrates how a road designed to a 20-year horizon induces traffic. The road is (re)-built with 20-year capacity, but is completed in 5 years. Drivers react to the additional road space by driving more, and expanded roadways built in recent years typically degrade the pedestrian experience, reducing the propensity of people to walk to schools, stores, or other destinations. Drivers also switch from alternative routes and earlier or later times for their commutes to fill the new capacity. The end result is that the road reaches its capacity in 10 years instead of 20.11
Click to enlarge
Source: VTPI. “Smart Congestion Relief.” 2013.
Retrofitting streets for pedestrians, cyclists and transit may require reducing or reallocating roadway vehicle capacity. While prevailing perceptions equate reduced vehicular capacity with increased traffic congestion, research suggests the opposite. Referred to as “traffic evaporation,” when road capacity is reduced (even in drastic amounts), vehicle volumes can actually respond by decreasing in similar proportion.8 Based on numerous case studies, “reductions in road capacity have not been followed by prolonged gridlock, and major increases in existing levels of congestion are typically only temporary...Instead, there is a fairly substantial body of evidence to suggest that some proportion of traffic effectively ‘disappears’...”9,10 Research suggests that the displaced traffic either (1) is absorbed by the surrounding street network, (2) shifts to another mode, or (3) the trip is altered (traveler changes destination or trip frequency).
Mode share for public transportation and bicycling has increased dramatically over the past five years.
To supplement existing traffic models, several other strategies should be considered that may present more accurate estimates for future traffic demand.
While the ITE Trip Generation Manual is a frequently cited source, in urban settings the manual’s outputs may not be a strong comparative match. To better meet the needs of urban settings, numerous research studies have been developed through universities and state DOTs that provide more precise trip generation rates
for urban settings.12
In many cities, traffic analysis requires the use of an “ambient growth factor” which reflects the underlying baseline traffic growth. This growth factor is often provided by city staff and is based on a moving average from past growth (typically 1%–2%). This growth factor is often considered to be an assumed positive factor but should be strongly reconsidered due to its potential inaccuracies given recent cultural trends. Growth factors should no longer be strictly based on multi-year moving averages since recent VMT trends have been shown to be volatile (or declining). While growth projection factors of 1%–2% seem minimal, it can have a significant cumulative impact over each year it is applied.
Several U.S. cities (Chicago, Minneapolis, San Francisco, and others) and states have developed specific mode targets to achieve within a set timeframe. For example, MassDOT has established a goal of tripling the number of trips by transit, bicycle and walking. San Francisco has established a goal of 50% non-auto trips by 2018. These goals provide a set objective and spur the rapid implementation of programs that seek to accomplish them. These types of underlying programmatic shifts are often not explicitly integrated into traffic modeling efforts, but can serve as a baseline from which to better understand potential future modal shifts.
Another underlying factor that may play a major role in changing future traffic demand is greenhouse gas (GHG) emissions. Several states across the United States are employing GHG targets that filter down into several more tangible objectives (such as mode share, VMT reduction and others). Massachusetts has established a target of 25% reduction in GHG by 2020 and 50% by 2050.13
If a project is determined to require an increase in roadway capacity, induced traffic demand should be considered a negative externality as a result. If the additional traffic demand created exceeds local policy thresholds (such as mode shift, as described above), it should be investigated if traffic can be mitigated through other non-roadway infrastructure strategies.
“Traffic Volume Trends,” accessed June 3, 2013.
Interim Guidance on the Application of Travel and Land Use Forecasting in NEPA (Washington, D.C.: USDOT, 2010).
Guide for the Preparation of Traffic Impact Studies (Sacramento: California Department of Transportation, 2002).
Traffic Impact Study Requirements (Salt Lake City: Utah Department of Transportation, 2004).
League of American Bicyclists, “Bicycle Commuting Data,” accessed June 3, 2013.
Douglass B. Lee, Lisa A. Klein, and Gregorio Camus, “Induced traffic and induced demand,” Transportation Research Record. No 1659 (1999): Appendix B.
Todd Litman, “Generated Traffic and Induced Travel,” ITE Journal 71 (2001): 38–47.
Trip Generation Rates for Urban Infill Land Uses in California (Sacramento: California Department of Transportation, 2008).
Massachusetts Department of Transportation, “MassDOT Goal: Triple Travel by Bicycle, Transit, Walking,” (October 2012).
Adapted from the Urban Street Design Guide, published by Island Press.