Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21457
Title: Real-time estimation of arterial travel times with spatial travel time covariance relationships
Authors: Chan, KS
Lam, WHK 
Tam, ML
Issue Date: 2009
Publisher: U.S. National Research Council, Transportation Research Board
Source: Transportation research record : journal of the Transportation Research Board, 2009, no. 2121, p. 102-109 How to cite?
Journal: Transportation research record : journal of the Transportation Research Board 
Abstract: This paper investigates the use of real-time automatic vehicle identification (AVI) data and an offline travel time database for real-time estimation of arterial travel times in Hong Kong, China. The offline database consists of average link travel times and spatial link travel time covariance matrices by time of day, day of week, and week of month. Three-month historical travel time estimates and real-time AVI data are adopted for calibration and updating of the spatial covariance relationships of link travel times on Hong Kong arterial roads. A case study has been carried out on a selected path in a Hong Kong urban area to evaluate the performance of three alternative methods for real-time estimation of arterial travel times: fixed offline database (Method 1), continuously updated offline database (Method 2), and continuously updated offline database generated by the nonparametric regression method (Method 3). The validation results show that the travel time estimation errors of Methods 2 and 3 are significantly reduced when compared with those using the fixed offline database.
URI: http://hdl.handle.net/10397/21457
ISSN: 0361-1981
DOI: 10.3141/2121-11
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

15
Last Week
0
Last month
0
Citations as of Feb 21, 2018

WEB OF SCIENCETM
Citations

15
Last Week
0
Last month
0
Citations as of Feb 23, 2018

Page view(s)

33
Last Week
0
Last month
Citations as of Feb 18, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.