Improving traffic simulation models and emissions models using on-board vehicle dynamics data

Recent developments in environmental policies require designers to consider the air quality impacts of new transportation management plans and changes in land use. Therefore, the traditional traffic simulation packages are being updated for improved estimation of vehicle emission estimates. Traditionally, emissions estimates in traffic simulation models were calculated based on an average traffic speed for roadway segments. However, recent research shows that vehicle emissions are a function of individual second-by-second vehicle dynamics or operating mode (cruise versus acceleration or a 1-second time series of acceleration and deceleration for example) and average speed is not a good predictor of vehicle emissions. New emissions models under development and final testing require microscopic traffic and vehicle operations data that are currently not collected or output from traffic simulation models. The accuracy of new emissions models requires consideration of the temporal profile of individual vehicle dynamics on the roadway or at intersections. Therefore, fundamental changes are necessary to fully integrate traffic simulation and vehicle emissions models for optimum functionality. This research makes use of two relatively large real world datasets of 1-Hz on-board, on-road data collected from 22 volunteer drivers to identify fundamental improvements to methods and theories commonly used in current traffic simulation and vehicle emissions modeling. Ultimately, this dissertation addresses these fundamental changes and recommends methods that should be used for the next generation of tools for environmental and transportation planners.