Transportation has been a significant contributor to greenhouse gas and criteria air pollutant emissions. Emission mitigation strategies are essential in reducing transportation’s impacts on our environment. In order to effectively develop and evaluate on-road emissions reduction strategies, accurate quantification of emissions is the critical first step. The accuracy and resolution of the traffic measures needed by the emission models will directly affect the emission estimation results. This dissertation investigates the application of traffic detection technologies to deriving the traffic measures needed for accurate on-road emissions estimation.
The inductive vehicle signature (IVS) system is identified as the most promising technology to couple with EPA’s latest MOVES emission model for estimating emissions accurately. Models and algorithms based on the IVS detection system are developed to generate the two most important traffic measures for emission estimation: vehicle mix and average speed. The performances of the models are verified using real-world field data.
Although average speed has been the most common input into emission models, the MOVES model is capable of using second-by-second vehicle speed trajectories to estimate emissions more accurately. Crowd sourced GPS data can also be used by emission models like MOVES to estimate emissions. In this study, we aim to answer two most fundamental questions: 1) how to use the GPS data, and 2) how the penetration rate of the GPS probes affects the emission results. It is found that emissions can be estimated with high accuracy and reliability with even a very small penetration rate of GPS probes.
We conclude that the IVS detection system and GPS probe data can be successfully applied to estimate accurate and reliable on-road emissions estimation. Discussions on the application of the models developed in this study to various scenarios are included.
