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In the past decade, a variety of questions have been raised concerning the effectiveness of high-occupancy vehicle (HOV) lanes. In Southern California, recent evaluation studies on HOV lanes confirm the effectiveness of HOV lanes in several ways. However, little research has been performed to evaluate the air quality benefits of HOV lanes. This paper describes a study that examines operational differences in traffic dynamics between HOV lanes and mixed-flow (MF) lanes and evaluates their impacts on vehicle emissions. Four general HOV lane scenarios were identified: underutilized, neutral, well utilized, and overutilized. Extensive driving trajectories in both lane types for each scenario were collected. Their speed profile and joint speed-acceleration frequency distribution were analyzed and compared. Vehicle emissions and fuel consumption were then estimated with a state-of-the-art modal emissions model. The results show that HOV lanes produce lower emission rates per vehicle per mile in most cases, except when they are underutilized. When normalized by average vehicle occupancy, HOV lanes produce much lower emission rates per the same amount of travel demand, on the order of 10% to 70%. In almost every case, HOV lanes produce less emissions mass on a per-lane basis than MF lanes. Southern California freeway lane performance matrices show that on a typical weekday during the summer of 2005, HOV lanes operated mostly under Scenarios 1 and 2 during peak periods. Overall, they were well utilized about 14% to 17% of the time. According to the emissions estimates, the HOV lanes are considered effective in reducing vehicle emissions.

Inter-Vehicle Communications (IVC) has the potential to play an important role in many future vehicle and traffic applications. Much of this will occur in the automated vehicle control and safety systems (AVCSS) arena, and to a lesser extent in the Advanced Transportation Management and Information Systems (ATMIS) arena. One such ATMIS application where IVC can be used is in decentralized traffic information systems. These systems do not require extensive infrastructure and management centers to collect and disseminate traffic information. Instead, information can be shared among vehicles through periodic broadcast messages. An adaptive dissemination mechanism for this is described in this paper. In the proposed design, each participating vehicle can adapt their transmission interval according to the current traffic speed and also disseminate the traffic information of different segments at different rates according to the distance to its current position. This dissemination scheme ensures the efficient distribution of information within the vehicle network. We have designed, developed, and tested this methodology using a unique traffic simulation environment that has been effectively augmented with wireless communication capability. Both analysis and simulation results show that the bandwidth that the system requires is far below the bandwidth of current 802.11 wireless links and the system can reach the accuracy requirement even at a low penetration rate.

Automatic Vehicle Location (AVL) systems are becoming increasingly common, especially for fleet monitoring/management applications such as probe vehicle operation. A large percentage of AVL systems report position and sometimes velocity on a periodic basis to a base station. However, sending data on a periodic basis can be costly and inefficient. It is more efficient to send data on an as-needed, aperiodic basis. It is possible to use real-time filtering algorithms to send trajectory data on an aperiodic basis using information on the spatial aspects of the roads and velocity of the vehicle. In many cases, it is only necessary to send new information when there is significant deviation both spatially and temporally. Further, velocity changes can trigger data transmissions. As part of the research described in this paper, aperiodic filtering techniques have been developed and applied to several AVL applications. Using these filtering algorithms, it is possible to decrease communications cost while improving route representation and predicted time of arrival. To test the effectiveness of the algorithms, an experimental AVL system was designed and developed. Further, the filtering techniques have been applied to large representative vehicle activity data sets, successfully reducing the communications and storage costs by 80 to 90%.

In recent years, automobile manufacturers have been producing gasoline-fueled vehicles that have very low tailpipe and evaporative emissions to meet stringent certification standards set by the U.S. Environmental Protection Agency and the California Air Resources Board. These extremely low-emitting vehicles are 98% to 99% cleaner than the catalyst-equipped vehicles produced in the mid-1980s. To understand better the emissions characteristics of these extremely low-emitting vehicles, as well as their potential impact on future air quality, researchers at the University of California, Riverside, have conducted a comprehensive study consisting of (a) an emissions measurement program, (b) the development of specific emissions models, and (c) the application of future emissions inventories to air quality models. Results have shown that in nearly all cases, these vehicles have emissions that are well below their stringent certification standards, and the vehicles continue to have low emissions as they age. On the basis of the measurement results, new modal emissions models have been created for both ultra-low-emission-certified vehicles and partial-zero-emission-certified vehicles. The model results compare well with actual measurements. With these models, it is possible to predict accurately future mobile source emissions inventories that will have an increasing number of these extremely low-emitting vehicles in the overall vehicle population. It is expected that the large penetration of these vehicles into the vehicle fleet will have a significant role in meeting ozone attainment levels in many regions.

The EPA is developing a new generation emissions inventory model, MOVES (Motor Vehicle Emissions Simulator). The first version of the model outputs fuel consumption based on available modal data. However, due to the limited heavy-duty vehicle data, MOVES rates need to be supplemented with rates determined with the Physical Emission Rate Estimator (PERE). PERE combines vehicle tractive power together with vehicle powertrain parameters specific to the class of vehicle; the vehicle weight, shape, engine type, and transmission. Analysis of in-use data for heavy-duty diesel tractortrailer vehicles, city transit diesel buses, and dynamometer non-road diesel engines has enabled a determination of diesel engine efficiency and friction and transmission shift schedules for these engines and vehicles. These model parameters and a comparison of the model results to measured fuel consumption and CO2 emissions are presented.

Mobile source emission models for years have depended on laboratory-based dynamometer data. Recently, however, portable emission measurement systems (PEMS) have become commercially available and in widespread use, and make on-road real-world measurements possible. As a result, the newest mobile source emission models (e.g., U.S. Environmental Protection Agency’s mobile vehicle emission simulator) are becoming increasingly dependent on PEMS data. Although on-road measurements are made under more realistic conditions than laboratory-based dynamometer test cycles, they introduce influencing variables that must be carefully measured for properly developed emission models. Further, test programs that simply measure in-use driving patterns of randomly selected vehicles will result in models that can effectively predict current-year emission inventories for typical driving conditions. However, when predicting more aggressive transportation operations than current typical operations (e.g., higher speeds, accelerations), the model predictions will be less certain. In this paper, various issues associated with on-road emission measurements and modeling are presented. Further, an example on-road emission data set and the reduction in estimation error through the addition of a short aggressive driving test to the in-use data are examined. On the basis of these results, recommendations are made on how to improve the on-road test programs for developing more robust emission models.