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Document Type:Latin Dissertation
Language of Document:English
Record Number:52566
Doc. No:TL22520
Call number:‭1462628‬
Main Entry:Anas Mohammad Mahmoud
Title & Author:Travel time estimation in congested urban networks using point detectors dataAnas Mohammad Mahmoud
College:Mississippi State University
Date:2009
Degree:M.S.
student score:2009
Page No:125
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.
Subject:Social sciences; Applied sciences; Artificial neural networks; Congestion management; Data cleaning; Intelligent transportation systems; Point detectors; Travel time estimation; Emergency evacuations; Civil engineering; Transportation planning; Computer science; 0984:Computer science; 0543:Civil engineering; 0709:Transportation planning
Added Entry:E. A. Hansen
Added Entry:Mississippi State University