Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80752
Title: A multiple regression approach for traffic flow estimation
Authors: Pun, LL 
Zhao, PX
Liu, XT 
Keywords: Traffic flow estimation
Topological and geometrical Properties
Correlation analysis
Multiple linear regression
Random forest
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2019, v. 7, p. 35998-36009 How to cite?
Journal: IEEE access 
Abstract: Traffic flow information is of great importance for transport planning and related research. The conventional methods of automated data collection, such as annual average daily traffic (AADT) data, are often restricted by limited installation, while the state-of-the-art sensing technologies (e.g., GPS) only reflect some types of traffic flow (e.g., taxi and bus). Complete coverage of traffic flow is still lacking, thus demanding a rigorous estimation model. Most studies dedicated to estimating the traffic flow of the entire road network rely on single to only a few properties of the road network and the results may not be promising. This paper presents an idea of integrating five topological measures and road length to estimate traffic flow based on a multiple regression approach. An empirical study in Hong Kong has been conducted with three types of traffic datasets, namely floating car, public transport route, and AADT. Six measures, namely degree, betweenness, closeness, page rank, clustering coefficient, and road length, are used for traffic flow estimation. It is found that each measure correlates differently for the three types of traffic data. Multiple regression approach is then conducted, including multiple linear regression and random forest. The results show that a combination of various topological and geometrical measures has proved to have a better performance in estimating traffic flow than that of a single measure. This paper is especially helpful for transport planners to estimate traffic flow based on correlation available but limited flow data with road network characteristics.
URI: http://hdl.handle.net/10397/80752
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2904645
Rights: © 2019 IEEE. Translations and content mining are permitted for academic research only.Personal use is also permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information
The following publication L. Pun, P. Zhao and X. Liu, "A Multiple Regression Approach for Traffic Flow Estimation," in IEEE Access, vol. 7, pp. 35998-36009, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2904645
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Pun_Regression_Flow_Traffic.pdf6.03 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

14
Citations as of Aug 21, 2019

Download(s)

13
Citations as of Aug 21, 2019

Google ScholarTM

Check

Altmetric


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