Travel time reliability assessment techniques for large-scale stochastic transportation networks
| dc.contributor.advisor | Waller, S. Travis | en |
| dc.contributor.committeeMember | Kockelman, Kara M. | en |
| dc.contributor.committeeMember | Zhang, Zhanmin | en |
| dc.contributor.committeeMember | Hasenbein, John J. | en |
| dc.contributor.committeeMember | Morton, David P. | en |
| dc.creator | Ng, Man Wo | en |
| dc.date.accessioned | 2010-10-07T17:18:25Z | en |
| dc.date.available | 2010-10-07T17:18:25Z | en |
| dc.date.available | 2010-10-07T17:18:31Z | en |
| dc.date.issued | 2010-05 | en |
| dc.date.submitted | May 2010 | en |
| dc.date.updated | 2010-10-07T17:18:31Z | en |
| dc.description | text | en |
| dc.description.abstract | Real-life transportation systems are subject to numerous uncertainties in their operation. Researchers have suggested various reliability measures to characterize their network-level performances. One of these measures is given by travel time reliability, defined as the probability that travel times remain below certain (acceptable) levels. Existing reliability assessment (and optimization) techniques tend to be computationally intensive. In this dissertation we develop computationally efficient alternatives. In particular, we make the following three contributions. In the first contribution, we present a novel reliability assessment methodology when the source of uncertainty is given by road capacities. More specifically, we present a method based on the theory of Fourier transforms to numerically approximate the probability density function of the (system-wide) travel time. The proposed methodology takes advantage of the established computational efficiency of the fast Fourier transform. In the second contribution, we relax the common assumption that probability distributions of the sources of uncertainties are known explicitly. In reality, this distribution may be unavailable (or inaccurate) as we may have no (or insufficient) data to calibrate the distributions. We present a new method to assess travel time reliability that is distribution-free in the sense that the methodology only requires that the first N moments (where N is any positive integer) of the travel time to be known and that the travel times reside in a set of known and bounded intervals. Instead of deriving exact probabilities on travel times exceeding certain thresholds via computationally intensive methods, we develop analytical probability inequalities to quickly obtain upper bounds on the desired probability. Because of the computationally intensive nature of (virtually all) existing reliability assessment techniques, the optimization of the reliability of transportation systems has generally been computationally prohibitive. The third and final contribution of this dissertation is the introduction of a new transportation network design model in which the objective is to minimize the unreliability of travel time. The computational requirements are shown to be much lower due to the assessment techniques developed in this dissertation. Moreover, numerical results suggest that it has the potential to form a computationally efficient proxy for current simulation-based network design models. | en |
| dc.description.department | Civil, Architectural, and Environmental Engineering | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/2152/ETD-UT-2010-05-1050 | en |
| dc.language.iso | eng | en |
| dc.subject | Travel time | en |
| dc.subject | Reliability | en |
| dc.subject | Stochastic, uncertain | en |
| dc.subject | Fast Fourier transform | en |
| dc.subject | Distribution-free probability inequalities | en |
| dc.subject | Network design problem | en |
| dc.subject | Genetic algorithm | en |
| dc.title | Travel time reliability assessment techniques for large-scale stochastic transportation networks | en |
| dc.type.genre | thesis | en |
| thesis.degree.department | Civil, Architectural, and Environmental Engineering | en |
| thesis.degree.discipline | Civil Engineering | en |
| thesis.degree.grantor | University of Texas at Austin | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |