Author: Pei-Wei Lin
Type: PhD Dissertation
Status: Completed
Year: 2006
Abstract: The purpose of this study is to develop an effective model and algorithm for estimating dynamic Origin-Destination demands for freeways. The primary challenge for this research subject lies in the fact that the number of unknown parameters is always more than the number of observable data, especially for a large network. Hence, the estimated O-D patterns may result in a large variance and insufficient reliability for use in practice. Besides, most existing approaches are grounded on the assumptions that a reliable initial O-D set is available and traffic volume data from detectors are accurate. However, in most highway network systems, both types of critical information are either unavailable or subjected to a significant level of measurement errors. To deal with those critical issues, this study has developed a set of dynamic models and solution algorithms for estimating freeway dynamic O-D matrices. The first extended model formulations can capture the speed discrepancy among drivers with an embedded travel time distribution function and the derivable interrelations between time varying ramp and mainline flows. These formulations also feature their best use of the available mainline information and travel time function, and hence substantially increase the system observability with fewer parameters.
Author: Nan Zou
Type: Ph. D. Dissertation
Status: Completed
Year: 2007
Abstract: Due to the increasing congestion in most urban networks, providing reliable trip times to commuters has emerged as one of the most critical challenges for all existing Advanced Traffic Information Systems (ATIS). However, predicting travel time is a very complex and difficult task, as the resulting accuracy varies with many variables of time-varying nature, including the day-to-day traffic demands, responses of individual drivers to daily commuting congestion, conditions of the road facility, weather, incidents, and reliability of available detectors. 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.
Author: Yue Liu
Type: Ph.D. Defense
Status: Completed
Year: 2009
Abstract: This research has focused on developing an advanced dynamic corridor traffic control system that can assist responsible traffic professionals in generating effective control strategies for contending with non-recurrent congestion that often concurrently plagues both the freeway and arterial systems. The developed system features its hierarchical operating structure that consists of an integrated-level control and a local-level module for bottleneck management. The primary function of the integrated-level control is to maximize the capacity utilization of the entire corridor under incident conditions with concurrently implemented strategies over dynamically computed windows, including diversion control at critical off-ramps, on-ramp metering, and optimal arterial signal timings. The system development process starts with design of a set of innovative network formulations that can accurately and efficiently capture the operational characteristics of traffic flows in the entire corridor optimization process.
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Author: Woon Kim
Type: Master’s thesis
Status: Completed
Year: 2007
Abstract: This study presents a set of models for predicting incident duration and identifying variables associated with the incident duration in the state of Maryland. The incident database for years 2003 to 2005 from the Maryland State Highway (MDSHA) database is used for model development, and year 2006 for the model validation. This study, based on the preliminary analysis with the Classification Tree method, has employed the Rule-Based Tree Model to develop the primary prediction model. To enhance the prediction accuracy for some incidents with complex nature or limited samples, the study has also proposed and calibrated several supplemental components based on the Multinomial Logit and Regression methods. Although the prediction accuracy could still be improved if a data set with better quality is available, the developed set of models offers an effective tool for responsible agencies to estimate the approximate duration of a detected incident, which is crucial in projecting the potential impacts on the highway network.