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Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2006 Symposium
Nevertheless, studies have shown that roadway congestion continues to get worse. For example, the Texas Transportation Institute (TTI) conducts an Annual Mobility Study that includes estimates of traffic congestion in many large cities and the impact on society (Schrank and Lomax, 2005). The study defines congestion as “slow speeds caused by heavy traffic and/or narrow roadways due to construction, incidents, or too few lanes for the demand.” Because traffic volume has increased faster than road capacity, congestion has gotten progressively worse, despite the push toward alternative modes of transportation, new technologies, innovative land-use patterns, and demand-management techniques.
Some of the major concerns raised by roadway congestion are impacts on energy consumption and air quality. The TTI Annual Mobility Study estimates that billions of gallons of fuel are wasted every year because of congestion (Schrank and Lomax, 2005). In addition, heavy congestion often leads to greater mobile-source emissions. One way to estimate the energy and emissions impacts of congestion is to examine velocity patterns of vehicles operating under different levels of congestion. Roadway congestion is often categorized by the “level of service” (LOS) (TRB, 1994). For freeways (i.e., uninterrupted flow), LOS can be represented as a ratio of traffic flow to roadway capacity. There are several different LOS values that are represented by the letters A through F. For each LOS, a typical vehicle-velocity trajectory will have different characteristics.
Examples of these velocity trajectories are shown in Figure 1 (EPA, 1997). Under LOS A, vehicles typically travel near the highway’s free-flow speed, with few acceleration/deceleration perturbations. As LOS conditions get progressively worse (i.e., LOS B, C, D, E, and F), vehicles travel at lower average speeds with more acceleration/deceleration events. For each representative vehicle-velocity trajectory (such as those shown in Figure 1), it is possible to estimate both fuel consumption and pollutant emissions. For automobiles, we are most often concerned about emissions of carbon monoxide (CO), hydrocarbons (HCs), oxides of nitrogen (NOx), and particulate matter.
Figure 2 shows examples of automobile fuel consumption and emission rates that correspond to the average speeds of the representative velocity trajectories shown in Figure 1. Fuel consumption and emission rates are normalized by distance traveled, given in units of grams per unit mile. As expected, when speeds are very low, vehicles do not travel very far; therefore, grams per mile emission rates are quite high. In fact, when a car is not moving, we get an infinite-distance normalized emission rate. Conversely, when vehicles travel at higher speeds, they experience higher engine load requirements and, therefore, have higher fuel consumption and emission rates. As a result, this type of speed-based emission-factor curve has a distinctive parabolic shape, with high emission rates on both ends and a minimum rate at moderate speeds of around 45 to 50 mph.
Figure 2 shows a fuel-consumption and emissions curve for a vehicle (an average “composite” vehicle representing the 2005 vehicle fleet in southern Cali-