What do road traffic congestion and energy have in common? The answer is simple: considerable amounts of fuel are wasted in congestion.  According to the Texas Transportation Institute (TTI), 2.8 billion gallons of fuel were wasted in 2007 in 439 U.S. urban areas due to congestion. That is about 2 percent of our annual gasoline needs, or close to $8 billion dollars at recent gasoline prices.

However, wasted fuel is but the smaller cost. The value of lost time is what really bites: approximately $80 billion in 2007. Therefore, eliminating congestion is actually worth close to $88 billion annually nationwide.

Congestion is caused by “[too] many people, too many trips over too short of a time period on a system that is too small” according to the TTI. This suggests that there are three solutions to congestion: (1) reduce the number of vehicles/trips, (2) spread those trips over longer periods of time, and/or (3) increase the size of the road system.

In this country, transportation administrators have traditionally sought to alleviate congestion by building more capacity. It seems fairly intuitive: double the capacity and you will halve congestion. However, a closer look at the factors that drive people to congested roads reveals a counterintuitive result: congestion is unlikely to be reduced in the same proportion that capacity is expanded.

When deciding whether to go out driving at peak time, a driver compares two costs:

1. Direct and indirect costs of congestion in the form of wasted fuel and time respectively, and
2. Opportunity costs of staying home and not doing (at least in person) whatever it is that was going to be done.

If the direct and indirect costs of congestion were higher than the opportunity costs, then a rational driver would stay home, and vice versa. For example, commuters who drive to work generally face a higher opportunity cost (being unable to interact directly with colleagues, or potentially losing their job) relative to the direct and indirect congestion costs (fuel wasted and time that could have been used in more valuable activities) , and therefore decide to go out and suffer the congestion.

Initially, increasing road capacity effectively lowers the costs of driving (less time and fuel wasted in congestion), thus drawing more drivers onto the roads. In other words, this lower cost meets the driving demand curve at a lower point, where the volume of traffic (demand for roads) will be higher. The new capacity encourages travelers who had previously avoided congestion through alternative modes of travel (such as mass transit) or travel times to take the highway, a phenomenon called “induced demand.” Over the long run, this induced traffic is estimated to fill up at least 40 percent of added urban road capacity. Thus, un-priced highway lane miles will have diminishing long term effects on congestion levels.[1]

Alternative policy tools focus on addressing the first two causes of congestion by either reducing the number of people/trips, or spreading those trips over longer periods of time.

Direct road pricing, for example, is a highly underutilized and proven near-term tool to reduce congestion. It provides a visible signal regarding the costs drivers are imposing on roads and other users, and influences driver behavior. Critically, road prices can capture the different costs imposed at different times of day. Prices can be varied to incorporate the costs of providing, maintaining, and operating the infrastructure as well as congestion impacts. This, in turn, can better inform individuals about the true costs of their travel choices. Travelers will then be able to make better choices about how and when they use existing transportation infrastructure. This should result in reduced wasted fuel and time, in addition to helping tackle U.S. oil dependence.

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[1] Kent M. Hymel, Kenneth A. Small and Kurt Van Dender, “Induced Demand and Rebound Effects in Road Transport,” May 1, 2009; Victoria Transport Policy Institute, “Rebound Effects: Implications for Transport Planning”, TDM Encyclopedia, Updated May 2010, www.vtpi.org/tdm/tdm64.htm.