Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81308
Title: Traffic data characterisation : review and challenges
Authors: Respati, S
Bhaskar, A
Chung, E 
Keywords: Traffic data
Traffic data characteristics
Traffic data challenges
Issue Date: 2018
Publisher: Elsevier
Source: Transportation research procedia, 2018, v. 34, p. 131-138 How to cite?
Journal: Transportation research procedia 
Abstract: The growth of technology provides a great amount of traffic data that have distinct characteristics. The absence of the comprehensive understanding of the characteristics and associated challenges leads to resources extravagance. In this paper, we develop data characterisation by disaggregating important traffic data features and present the associated data challenges to provide better insights of traffic data and expand traffic data usage. The paper outlines the opportunity to maximize the data utilisation.
Description: International Symposium of Transport Simulation (ISTS'18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS'18) - Emerging Transport Technologies for Next Generation Mobility, Aug 06-08, 2018 Ehime University, Matsuyama, Japan
URI: http://hdl.handle.net/10397/81308
EISSN: 2352-1465
DOI: 10.1016/j.trpro.2018.11.024
Rights: © 2018 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
The following publication Respati, S., Bhaskar, A., & Chung, E. (2018). Traffic data characterisation: review and challenges. Transportation Research Procedia, 34, 131-138 is available at https://dx.doi.org/10.1016/j.trpro.2018.11.024
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
Respati_Traffic_Data_Characterisation.pdf573.71 kBAdobe 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)

8
Citations as of Oct 15, 2019

Download(s)

4
Citations as of Oct 15, 2019

Google ScholarTM

Check

Altmetric


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