QUT ePrints

Performance evaluation of an adaptive travel time prediction model

Bajwa, Shamas, Chung, Edward, & Kuwahara, Masao (2005) Performance evaluation of an adaptive travel time prediction model. In Smid, E (Ed.) Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE, IEEE, Austria, Vienna, pp. 1000-1005.

View at publisher

Abstract

This paper presents a travel time prediction model and evaluates its performance and transferability. Advanced Travelers Information Systems (ATIS) are gaining more and more importance, increasing the need for accurate, timely and useful information to the travelers. Travel time information quantifies the traffic condition in an easy to understand way for the users. The proposed travel time prediction model is based on an efficient use of nearest neighbor search. The model is calibrated for optimal performance using Genetic Algorithms. Results indicate better performance by using the proposed model than the presently used naïve model.

Impact and interest:

8 citations in Scopus
Search Google Scholar™
0 citations in Web of Science®

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

452 since deposited on 04 Oct 2010
120 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 37430
Item Type: Conference Paper
Keywords: driver information systems, genetic algorithms, prediction theory, transportation
ISBN: 0-7803-9215-9
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Simulation and Modelling (080110)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Deposited On: 04 Oct 2010 16:42
Last Modified: 01 Mar 2012 00:06

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page