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 ability of traffic detection technologies to provide the traffic measures needed for accurate on-road emissions estimation. A review of traffic detection technologies is provided with insight into their capability and suitability for estimating emissions. The Inductive Vehicle Signature (IVS) system is identified as currently the most promising technology to couple with EPA's latest MOVES emission model for estimating emissions. 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 data. Assuming the IVS system and the models developed are deployed to generate vehicle mix and average speeds, the accuracy and reliability of the emissions estimation results based on these traffic measures are evaluated by simulating the operations of the models in the field using NGSIM data. Very promising results are obtained, which clearly demonstrates the capability of the IVS system for on-road emissions estimation. A Real-Time Emissions Estimation and Monitoring System based on the IVS technology is implemented on the I-405 freeway to estimate operational emissions on the road in real-time. 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. Vehicle speed trajectories are becoming increasingly available thanks to the proliferation of GPS-enabled personal navigation devices and smartphones. Crowd sourced GPS data can also be used by emission models like MOVES to estimate emissions. This dissertation studies the use of a limited number of GPS speed trajectories to estimate emissions for all traffic on the road. Two fundamental questions are answered by this work: 1) how can GPS data be used for emissions estimation, and 2) how does the penetration rate of the GPS probes affect the emission results. With the methods proposed in this study, it is found that emissions can be estimated with high accuracy and reliability with even a very small penetration rate of GPS probes, when combined with the vehicle mix data generated from the IVS system. Discussions on the applications of the proposed systems and methods to various emissions analysis scenarios are also provided in this dissertation.