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

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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%.

Vehicle fuel consumption and emissions are directly related to the acceleration/deceleration patterns and the idling period. In order to reduce emissions and improve fuel economy, sharp acceleration/deceleration and idling should be avoided as much as possible. Unlike on freeways, traffic on signalized corridors suffers from increased fuel consumption and emissions due to idling and acceleration/deceleration maneuvers at traffic signals. By taking advantage of the recent developments in communication technology between vehicles and roadside infrastructure, it is possible for vehicles to receive the signal phase and timing information well in advance of approaching a signalized intersection. Based on this traffic signal information, we have developed arterial velocity planning algorithms that give dynamic speed advice to the driver so that the probability of having a green light is maximized when approaching signalized intersections. The algorithms are aimed at minimizing the acceleration/deceleration rates while ensuring that the vehicle never exceeds the speed limit, and that it will pass through intersections without coming to a stop. Using a stochastic simulation technique, the algorithms are used to generate sample vehicle velocity profiles along a 10-intersection signalized corridor. The resulting vehicle fuel consumption and emissions from these velocity profiles are calculated using a modal emissions model, and then compared with those from a typical velocity profile of vehicles without velocity planning. The energy/emission savings for vehicles with velocity planning are found to be 12-14%.

21vehicle) and a destination. More recently, the newer systems have become capable of incorporating real-time traffic information and provide the ability to find the shortest-duration route, in addition to the standard shortest-distance route. Moreover, because of high fuel prices and increased public awareness of climate change and other environmental problems, researchers are now investigating new navigation methodologies that select a route that requires the least amount of fuel or that produces the least amount of emissions (5, 6).Studies have shown that the selection of different travel routes between the same origin-destination (O-D) pair can result in significant differences in the amount of fuel consumed and the amount of emissions emitted (7,8). Researchers at the University of California at Riverside have developed new advanced navigation systems that provide users the ability to select not only shortest-distance and shortest-duration routes but also routes that minimizes fuel consumption as well as greenhouse gas (in particular, CO 2 ) and pollutant emissions (6). These new navigation techniques, so-called ecorouting, combine sophisticated mobile-source energy and emission models with route minimization algorithms that are used for navigational purposes. In the initial work, fuel consumption and emission attributes are estimated for freeway links on the basis of the measured traffic volume, density, and average speed. Instead of standard travel time or distance attributes, these link attributes are then used as cost factors when an optimal route for any particular trip is selected.In addition to roadway congestion, as reflected by traffic speed and density, it is expected that road grade also plays a role in the ecorouting methodology. Studies have shown that road grade has a significant effect on the energy consumption and emissions of lightduty vehicles (9-11). For example, the level of fuel consumption and the CO 2 emission rate (in grams per mile) of a vehicle are higher on upgrades than on a flat road, as the vehicle needs more power to work against the gravitational force induced by the upgrades. However, fuel consumption rates are lower on downgrades than on a flat road, as the gravitational force works in favor of the vehicle.So far, the assessment of the road grade effect has been at the roadway link level and has characterized the energy and emission factors of vehicles for different (both positive and negative) road grade levels. However, the road grade effect at the route level has not been studied in detail. As in roadway navigation, the change in elevation between an O-D pair is always conserved, no matter which routes are taken. Thus, high energy and emission rates on upgrade segments could well be offset by low energy and emission rates on downgrade segments. As a result, the total energy and emissions on a hilly route might turn out to be similar to those on a flat route, with everything else being equal.This study evaluated the effect of road grade on light-duty vehicle fuel consumpti...

In recent years, shared-use vehicle systems have garnered a great deal of interest and activity internationally as an innovative mobility solution. In general, shared-use vehicle systems consist of a fleet of vehicles that are used by several different individuals throughout the day. Shared-use vehicles offer the convenience of the private automobile and more flexibility than public transportation alone. These systems are attractive since they offer the potential to lower a user’s transportation costs; reduce the need for parking spaces in a community; improve overall air quality; and facilitate access to and encourage use of other transportation modes such as rail transit. Shared-use vehicle systems take many forms, ranging from neighborhood carsharing to classic station car models. Given the recent proliferation in system approaches, it is useful to establish a classification system or framework for characterizing these programs. The classification system presented here outlines key program elements that can help policy makers and practitioners characterize and evaluate various aspects of this rapidly evolving field. Further, it helps researchers analyze and compare the various models, including their similarities, differences, and benefits. A shared-use vehicle classification system is provided that describes existing and evolving models; examples are provided of each. It is argued that carsharing and station car concepts can be viewed as two ends of a continuum, sharing many similarities, rather than as separate concepts. Indeed, many existing shared-use vehicle systems can be viewed as hybrid systems, exhibiting key characteristics of both concepts.