UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item A Reliable Travel Time Prediction System With Sparsely Distributed Detectors(2007-05-22) Zou, Nan; Chang, Gang-Len; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study aims to develop a travel time prediction system that needs only a small number of reliable traffic detectors to perform accurate real-time travel time predictions under recurrent traffic conditions. To ensure its effectiveness, the proposed system consists of three principle modules: travel time estimation module, travel time prediction module, and the missing data estimation module. The travel time estimation module with its specially designed hybrid structure is responsible for estimating travel times for traffic scenarios with or without sufficient field observations, and for supplying the estimated results to support the prediction module. The travel time prediction module is developed to take full advantage of various available information, including historical travel times, geometric features, and daily/weekly traffic patterns. It can effectively deal with various traffic patterns with its multiple embedded models, including the primary module of a multi-topology Neural Network model with a rule-based clustering function and the supplemental module of an enhanced k-Nearest Neighbor model. To contend with the missing data issue, which occurs frequently in any real-world system, this study incorporates a missing data estimation module in the travel time prediction system, which is based on the multiple imputation technique to estimate both the short- and long-term missing traffic data so as to avoid interrupting the operations. The system developed in this study has been implemented with data from 10 roadside detectors on a 25-mile stretch of I-70 eastbound, and its performance has been tested against actual travel time data collected by an independent evaluation team. Results of extensive evaluation have indicated that the developed system is capable of generating reliable prediction of travel times under various types of traffic conditions and outperforms both state-of-the-practice and state-of-the-art models in the literature. Its embedded missing data estimation models also top existing methods and are able to maintain the prediction system under a reliable state when one of its detectors at a key location experience the data missing rate from 20% to 100% during uncongested, congested and transition periods.Item PANEL SURVEY ESTIMATION IN THE PRESENCE OF LATE REPORTING AND NONRESPONSE(2004-08-06) Copeland, Kennon R; Lahiri, Partha; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Estimates from economic panel surveys are generally required to be published soon after the survey reference period, resulting in missing data due to late reporting as well as nonresponse. Estimators currently in use make some attempt to correct for the impact of missing data. However, these approaches tend to simplify the assumed nature of the missing data and often ignore a portion of the reported data for the reference period. Discrepancies between preliminary and revised estimates highlight the inability of the estimation methodology to correct for all error due to late reporting. The current model for one economic panel survey, the Current Employment Statistics survey, is examined to identify factors related to potential model misspecification error, leading to identification of an extended model. An approach is developed to utilize all reported data from the current and prior reference periods, through missing data imputation. Two alternatives to the current models that assume growth rates are related to recent reported data and reporting patterns are developed, one a simple proportional model, the other a hierarchical fixed effects model. Estimation under the models is carried out and performance compared to that of the current estimator through use of historical data from the survey. Results, although not statistically significant, suggest the potential associated with use of reported data from recent time periods in the working model, especially for smaller establishments. A logistic model for predicting likelihood of late reporting for sample units that did not report for preliminary estimates is also developed. The model uses a combination of operational, respondent, and environmental factors identified from a reporting pattern profile. Predicted conditional late reporting rates obtained under the model are compared to actual rates through use of historical information for the survey. Results indicate the appropriateness of the parameters chosen and general ability of the model to predict final reporting status. Such a model has the potential to provide information to survey managers for addressing late reporting and nonresponse.