Thesis (M.S.)--Montana State University--Bozeman, 2006. / Typescript. Chairperson, Graduate Committee: James P. Becker. Includes bibliographical references (leaves 120-121).

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (site viewed on Feb. 8, 2007). PDF text: vi, 106 p. : ill. (some col.). UMI publication number: AAT 3216416. Includes bibliographical references. Also available in microfilm and microfiche format.

Thesis (Ph. D.)--University of Texas at Dallas, 2006. / Includes vita. Includes bibliographical references (leaves 173-178).

Thesis (M.Phil.)--City University of Hong Kong, 2006. / "Submitted to Department of Physics and Materials Science in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 106-116)

Flow speed describes general traffic operation conditions on a segment of roadway. It is also used to diagnose special conditions such as congestion and incidents. Accurate speed estimation plays a critical role in traffic management or traveler information systems. Data from loop detectors have been primary sources for traffic information, and single loop are the predominant loop detector type in many places. However, single loop detectors do not produce speed output. Therefore, speed estimation using single loop outputs has been an important issue for decades. This dissertation research presents two methodologies for speed estimation using single loop outputs. Based on findings from past studies and examinations in this research, it is verified that speed estimation is a nonlinear system under various traffic conditions. Thus, a methodology of using Unscented Kalman Filter (UKF) is first proposed for such a system. The UKF is a parametric filtering technique that is suitable for nonlinear problems. Through an Unscented Transformation (UT), the UKF is able to capture the posterior mean and covariance of a Gaussian random variable accurately for a nonlinear system without linearization. This research further shows that speed estimation is a nonlinear non-Gaussian system. However, Kalman filters including the UKF are established based on the Gaussian assumption. Thus, another nonlinear filtering technique for non-Gaussian systems, the Particle Filter (PF), is introduced. By combining the strengths of both the PF and the UKF, the second speed estimation methodology - Unscented Particle Filter (UPF) is proposed for speed estimation. The use of the UPF avoids the limitations of the UKF and the PF. Detector data are collected from multiple freeway locations and the microscopic traffic simulation program CORSIM. The developed methods are applied to the collected data for speed estimation. The results show that both proposed methods have high accuracies of speed estimation. Between the UKF and the UPF, the UPF has better performance but has higher computation cost. The improvement of speed estimation will benefit real-time traffic operations by improving the performance of applications such as travel time estimation using a series of single loops in the network, incident detection, and large truck volume estimation. Therefore, the work enables traffic analysts to use single loop outputs in a more cost-effective way.

Flow speed describes general traffic operation conditions on a segment of roadway. It is also used to diagnose special conditions such as congestion and incidents. Accurate speed estimation plays a critical role in traffic management or traveler information systems. Data from loop detectors have been primary sources for traffic information, and single loop are the predominant loop detector type in many places. However, single loop detectors do not produce speed output. Therefore, speed estimation using single loop outputs has been an important issue for decades. This dissertation research presents two methodologies for speed estimation using single loop outputs. Based on findings from past studies and examinations in this research, it is verified that speed estimation is a nonlinear system under various traffic conditions. Thus, a methodology of using Unscented Kalman Filter (UKF) is first proposed for such a system. The UKF is a parametric filtering technique that is suitable for nonlinear problems. Through an Unscented Transformation (UT), the UKF is able to capture the posterior mean and covariance of a Gaussian random variable accurately for a nonlinear system without linearization. This research further shows that speed estimation is a nonlinear non-Gaussian system. However, Kalman filters including the UKF are established based on the Gaussian assumption. Thus, another nonlinear filtering technique for non-Gaussian systems, the Particle Filter (PF), is introduced. By combining the strengths of both the PF and the UKF, the second speed estimation methodology—Unscented Particle Filter (UPF) is proposed for speed estimation. The use of the UPF avoids the limitations of the UKF and the PF. Detector data are collected from multiple freeway locations and the microscopic traffic simulation program CORSIM. The developed methods are applied to the collected data for speed estimation. The results show that both proposed methods have high accuracies of speed estimation. Between the UKF and the UPF, the UPF has better performance but has higher computation cost. The improvement of speed estimation will benefit real-time traffic operations by improving the performance of applications such as travel time estimation using a series of single loops in the network, incident detection, and large truck volume estimation. Therefore, the work enables traffic analysts to use single loop outputs in a more cost-effective way.

Time-of-Flight (TOF) and Interrupted-Field Time-of-Flight (IFTOF) measurements were performed repeatedly on several different samples of amorphous Selenium (a-Se) alloys as they aged from deposition or after annealing above the glass transition temperature (Tg) in order to examine the relaxation of the electrical properties. The mobility was found to relax slightly, but the relaxation did not fit well to a stretched exponential. The increase in the mobility for electrons was significantly more than the increase in mobility for holes in all sample compositions measured. For electrons, the mobility increased by 20-40%, whereas for holes, the mobility only increased by less than 10%. The relaxation of the lifetime, on the other hand, fit well to a stretched exponential. Furthermore, the overall increase in lifetime as it relaxed was much greater than the increase in the mobility. The average increase in lifetime was 85% for holes and 45% for electrons. The stretched exponential fits consisted of two important factors: the structural relaxation time ôsr and the stretching factor â. For a given a-Se alloy, ôsr was approximately the same for relaxation from both immediately after sample deposition, and annealing above Tg, indicating that the relaxation is readily repeatable and has the same physical origin. The relaxation was found to be dependent on the a-Se alloy composition. While the general shape of the relaxation was consistently a stretched exponential, ôsr increased with increasing arsenic (As) concentration in the alloy, while â remained constant between 0.6-0.7. Additionally, ôsr was found to be the same for both electron and hole relaxations for a given composition. Thus, the relaxation in both the electron and hole lifetime seems to be controlled by the same structural relaxation process, that is, the electron and hole traps are structural in origin.

Time-of-Flight (TOF) and Interrupted-Field Time-of-Flight (IFTOF) measurements were performed repeatedly on several different samples of amorphous Selenium (a-Se) alloys as they aged from deposition or after annealing above the glass transition temperature (Tg) in order to examine the relaxation of the electrical properties. The mobility was found to relax slightly, but the relaxation did not fit well to a stretched exponential. The increase in the mobility for electrons was significantly more than the increase in mobility for holes in all sample compositions measured. For electrons, the mobility increased by 20-40%, whereas for holes, the mobility only increased by less than 10%. The relaxation of the lifetime, on the other hand, fit well to a stretched exponential. Furthermore, the overall increase in lifetime as it relaxed was much greater than the increase in the mobility. The average increase in lifetime was 85% for holes and 45% for electrons. The stretched exponential fits consisted of two important factors: the structural relaxation time ôsr and the stretching factor â. For a given a-Se alloy, ôsr was approximately the same for relaxation from both immediately after sample deposition, and annealing above Tg, indicating that the relaxation is readily repeatable and has the same physical origin. The relaxation was found to be dependent on the a-Se alloy composition. While the general shape of the relaxation was consistently a stretched exponential, ôsr increased with increasing arsenic (As) concentration in the alloy, while â remained constant between 0.6-0.7. Additionally, ôsr was found to be the same for both electron and hole relaxations for a given composition. Thus, the relaxation in both the electron and hole lifetime seems to be controlled by the same structural relaxation process, that is, the electron and hole traps are structural in origin.

Flow speed describes general traffic operation conditions on a segment of roadway. It is also used to diagnose special conditions such as congestion and incidents. Accurate speed estimation plays a critical role in traffic management or traveler information systems. Data from loop detectors have been primary sources for traffic information, and single loop are the predominant loop detector type in many places. However, single loop detectors do not produce speed output. Therefore, speed estimation using single loop outputs has been an important issue for decades. This dissertation research presents two methodologies for speed estimation using single loop outputs. Based on findings from past studies and examinations in this research, it is verified that speed estimation is a nonlinear system under various traffic conditions. Thus, a methodology of using Unscented Kalman Filter (UKF) is first proposed for such a system. The UKF is a parametric filtering technique that is suitable for nonlinear problems. Through an Unscented Transformation (UT), the UKF is able to capture the posterior mean and covariance of a Gaussian random variable accurately for a nonlinear system without linearization. This research further shows that speed estimation is a nonlinear non-Gaussian system. However, Kalman filters including the UKF are established based on the Gaussian assumption. Thus, another nonlinear filtering technique for non-Gaussian systems, the Particle Filter (PF), is introduced. By combining the strengths of both the PF and the UKF, the second speed estimation methodology—Unscented Particle Filter (UPF) is proposed for speed estimation. The use of the UPF avoids the limitations of the UKF and the PF. Detector data are collected from multiple freeway locations and the microscopic traffic simulation program CORSIM. The developed methods are applied to the collected data for speed estimation. The results show that both proposed methods have high accuracies of speed estimation. Between the UKF and the UPF, the UPF has better performance but has higher computation cost. The improvement of speed estimation will benefit real-time traffic operations by improving the performance of applications such as travel time estimation using a series of single loops in the network, incident detection, and large truck volume estimation. Therefore, the work enables traffic analysts to use single loop outputs in a more cost-effective way.

Flow speed describes general traffic operation conditions on a segment of roadway. It is also used to diagnose special conditions such as congestion and incidents. Accurate speed estimation plays a critical role in traffic management or traveler information systems. Data from loop detectors have been primary sources for traffic information, and single loop are the predominant loop detector type in many places. However, single loop detectors do not produce speed output. Therefore, speed estimation using single loop outputs has been an important issue for decades. This dissertation research presents two methodologies for speed estimation using single loop outputs. Based on findings from past studies and examinations in this research, it is verified that speed estimation is a nonlinear system under various traffic conditions. Thus, a methodology of using Unscented Kalman Filter (UKF) is first proposed for such a system. The UKF is a parametric filtering technique that is suitable for nonlinear problems. Through an Unscented Transformation (UT), the UKF is able to capture the posterior mean and covariance of a Gaussian random variable accurately for a nonlinear system without linearization. This research further shows that speed estimation is a nonlinear non-Gaussian system. However, Kalman filters including the UKF are established based on the Gaussian assumption. Thus, another nonlinear filtering technique for non-Gaussian systems, the Particle Filter (PF), is introduced. By combining the strengths of both the PF and the UKF, the second speed estimation methodology - Unscented Particle Filter (UPF) is proposed for speed estimation. The use of the UPF avoids the limitations of the UKF and the PF. Detector data are collected from multiple freeway locations and the microscopic traffic simulation program CORSIM. The developed methods are applied to the collected data for speed estimation. The results show that both proposed methods have high accuracies of speed estimation. Between the UKF and the UPF, the UPF has better performance but has higher computation cost. The improvement of speed estimation will benefit real-time traffic operations by improving the performance of applications such as travel time estimation using a series of single loops in the network, incident detection, and large truck volume estimation. Therefore, the work enables traffic analysts to use single loop outputs in a more cost-effective way.