Although these options look promising, they are unlikely to make a great impact in the near term. Some of them (e.g., fuel-cell vehicles) are still in their early stages of technology development and probably will need a dramatic breakthrough before they can be fully implemented. For those that are technology-ready and have started to enter the market (e.g., hybrid vehicles and alternative fuels), it will still probably take several years for a majority of the existing fleet to be turned over before a significant impact on CO 2 can be seen. That being said, it can be pointed out that comparatively little attention has been given to CO 2 emissions associated with traffic congestion and possible shortterm CO 2 reductions as a result of improved traffic operations. Traffic congestion can be considered as a supply management problem. The transportation infrastructure (i.e., roadways) can be considered as supply for use by drivers (demand). If these supplies are limited in terms of capacity and demand is high, congestion is likely to occur.Several studies have shown that roadway congestion is continuing to get worse. For example, the Texas Transportation Institute (TTI) conducts an urban mobility study that includes estimates of traffic congestion in many large cities and the impact on society (3). The study defines congestion as "slow speeds caused by heavy traffic or narrow roadways or both due to construction, incidents, or too few lanes for the demand." Because traffic volume has increased faster than road capacity, congestion has become progressively worse despite the push toward alternative modes of transportation, new technologies, innovative land use patterns, and demand management techniques.It is commonly known that as traffic congestion increases, CO 2 emissions (and in parallel, fuel consumption) also increase. In general, CO 2 emissions and fuel consumption are sensitive to the type of driving that occurs. Highlighted as part of many "eco-driving" strategies, traveling at a steady-state velocity will give much lower emissions and fuel consumption compared with a stop-and-go driving pattern. By decreasing the stop-and-go driving that is associated with congested traffic, CO 2 emissions can be reduced. However, it is not clear to what degree various congestion mitigation programs will affect CO 2 emissions. CO 2 emissions are examined here as a function of traffic congestion. After some background information on modeling tools and traffic information data used for analysis, the basis of the congestion analysis is developed, followed by real-world congestion analyses.
BACKGROUND
There are a variety of strategies that are now being considered to reduce fuel consumption and carbon dioxide (CO 2 ) emissions from the transportation sector. One strategy that is gaining interest worldwide is known as "eco-driving". Eco-driving typically consists of changing a person's driving behavior based on general (static) advice to the driver, such as accelerating slowly, driving smoothly, reducing high speeds, etc. Taking this one-step further, it is possible to provide realtime advice to drivers based on changing traffic and infrastructure conditions for even greater fuel and emission savings. This concept of dynamic eco-driving takes advantage of real-time traffic sensing and infrastructure information, which can then be communicated to a vehicle with a goal of reducing fuel consumption and emissions. In this paper, we consider dynamic eco-driving in an arterial corridor with traffic signals, where signal phase and timing information of a traffic light is provided to the vehicle. The vehicle can then adjust its velocity while traveling through a signalized corridor with the goal of minimizing fuel consumption and emissions. A dynamic ecodriving velocity planning algorithm has been developed and is described herein. This algorithm has then been tested in simulation, showing initial fuel economy and CO 2 improvements of around 12%.
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