Forecasting Dynamic Passenger Car Activity at Signalized Intersections for Enhanced Mobile Source Emission Modeling

Current research suggests that vehicle emission rates are highly correlated with modal vehicle activity and that specific instances of load-induced enrichment may contribute a disproportionate share of motor vehicle emissions. Consequently, researchers have been developing a variety of modal modeling approaches to transportation-related air quality modeling. Modal models predict emissions as a function of specific operating modes or engine load surrogates that represent the on-road operating conditions leading to high instantaneous emissions rates, such as hard accelerations and decelerations. Such models should be much more accurate in making realistic estimates of mobile source contribution to local and regional air quality. These new models typically require that vehicle activity be input by fraction of time spent in these different operating modes. However, the ability to realistically model microscopic on-road modal vehicle activity currently limits the implementation of these models. To provide better estimates of microscopic vehicle activity, field studies using laser rangefinding devices were undertaken to quantify actual driving patterns along signalized arterials and at signal-controlled intersections in Atlanta, Georgia. Data were analyzed to determine the fractions of vehicle activity spent in different operating modes, especially those likely to lead to high engine load and elevated emissions. Using binary recursive partitioning tree-based regression methods, analyses indicated that roadway grade, vehicle queue position, traffic volume, and distance to the downstream intersection are the most critical variables in influencing the modal activity. A significant amount of onroad activity was also found to be outside of the range of activity represented in the Federal Test Procedure. Statistical analysis of the data yielded a model for predicting link-based microscopic vehicle activity summaries based on geometric and operational characteristics of the roadway. Research results will provide the ability to estimate microscopic vehicle activity as input to both local and regional transportation-related air quality models. Findings may also enhance current methods for estimating capacity and modeling traffic flow and may have applications for intelligent transportation systems.