Forecasting dynamic vehicular activity on freeways: Bridging the gap between travel demand and emerging emissions models

New emissions models for predicting carbon monoxide, hydrocarbons, and oxides of nitrogen require as input not only average speed but various measures of dynamic vehicular activity such as accelerations, decelerations, and idle events as well. Current travel demand modeling of transportation networks does not provide estimates of dynamic vehicular activity but, instead, forecasts traffic volumes and travel speeds. Simulation models could provide estimates of dynamic vehicular activity, but simulation models are not used or validated for the development of regional emissions inventories. Until simulation models are used for regional planning purposes, improvements to travel demand models (TDMs) must be forthcoming if the benefits of new emissions models are to be realized. There are at least two solutions for bridging the gap between TDM outputs and new, data-intensive emissions models. The first solution is the development of statistical models that forecast dynamic vehicular activity as a function of TDM outputs: average traffic speeds and volumes. The second solution is the identification of mutually exclusive and collectively exhaustive regions of the speed-flow regime whereby representative dynamic driving sequences or cycles are characteristically different, particularly with respect to vehicular emissions. The present focus is on dynamic activity on freeways. A brief background of the research problem is first provided, along with stated research goals. The field study conducted to collect the necessary data on freeways is then described. The statistical task of identifying homogeneous regions of the speed-flow regime with respect to emissions activity is then discussed. Finally, the process by which typical dynamic driving activity is generated is given, followed by research conclusions and issues that require further study.