Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Hansen, A. Erin

Committee Member

Boggess, E. Julian

Committee Member

Zhang, Li

Date of Degree

5-2-2009

Document Type

Graduate Thesis - Open Access

Major

Computer Science

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

A model for estimating travel time on short arterial links of congested urban networks, using currently available technology, is introduced in this thesis. The objective is to estimate travel time, with an acceptable level of accuracy for real-life traffic problems, such as congestion management and emergency evacuation. To achieve this research objective, various travel time estimation methods, including highway trajectories, multiple linear regression (MLR), artificial neural networks (ANN) and K –nearest neighbor (K-NN) were applied and tested on the same dataset. The results demonstrate that ANN and K-NN methods outperform linear methods by a significant margin, also, show particularly good performance in detecting congested intervals. To ensure the quality of the analysis results, set of procedures and algorithms based on traffic flow theory and test field information, were introduced to validate and clean the data used to build, train and test the different models.

URI

https://hdl.handle.net/11668/15156

Download

Share

COinS