Integrating data-driven and simulation models to predict traffic state affected by road incidents

Shafiei, S; Mihăiţă, AS; Nguyen, H; Cai, C

Integrating data-driven and simulation models to predict traffic state affected by road incidents

Shafiei, S Mihăiţă, AS Nguyen, H Cai, C

Permalink

Publisher:: TAYLOR & FRANCIS LTD
Publication Type:: Journal Article
Citation:: Transportation Letters, 2021
Issue Date:: 2021-01-01

Recently Added

	Filename	Description	Size
	2021_TrLetters_Sajjad_PREPRINT.pdf	Submitted version	2.16 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is new to OPUS and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Shafiei, S
dc.contributor.author	Mihăiţă, AS
dc.contributor.author	Nguyen, H
dc.contributor.author	Cai, C
dc.date.accessioned	2022-01-06T06:31:31Z
dc.date.available	2022-01-06T06:31:31Z
dc.date.issued	2021-01-01
dc.identifier.citation	Transportation Letters, 2021
dc.identifier.issn	1942-7867
dc.identifier.issn	1942-7875
dc.identifier.uri	http://hdl.handle.net/10453/152766
dc.description.abstract	Predicting the traffic conditions in urban networks is a priority for traffic management centres. This becomes very challenging, especially when the network is affected by traffic incidents that vary in both time and space. Although data-driven modelling can be considered an ideal tool for short-term traffic predictions, its performance is severely degraded if little historical traffic information is available under incident conditions. This paper addresses this challenge by integrating data-driven and traffic simulation modelling approaches. Instead of directly predicting the traffic states using limited historical data, we employ a traffic simulation reinforced by data-driven models. The traffic simulation uses newly reported incident information and the estimated origin-destination (OD) demand flows to capture the complex interaction between drivers and road network, and predicts traffic states under extreme conditions. We showcase the capability of the proposed data-driven enforced traffic simulation platform for incident impact analysis in a real-life sub-network in Sydney, Australia.
dc.language	English
dc.publisher	TAYLOR & FRANCIS LTD
dc.relation.ispartof	Transportation Letters
dc.relation.isbasedon	10.1080/19427867.2021.1916284
dc.rights	This is an Accepted Manuscript of an article published by Taylor & Francis in Transportation Letters On: 01 Jan 2021 Available online: 10.1080/19427867.2021.1916284 http://authorservices.taylorandfrancis.com/sharing-your-work/
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Integrating data-driven and simulation models to predict traffic state affected by road incidents
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	recently_added	*
dc.date.updated	2022-01-06T06:31:30Z
pubs.publication-status	Published

Abstract:

Predicting the traffic conditions in urban networks is a priority for traffic management centres. This becomes very challenging, especially when the network is affected by traffic incidents that vary in both time and space. Although data-driven modelling can be considered an ideal tool for short-term traffic predictions, its performance is severely degraded if little historical traffic information is available under incident conditions. This paper addresses this challenge by integrating data-driven and traffic simulation modelling approaches. Instead of directly predicting the traffic states using limited historical data, we employ a traffic simulation reinforced by data-driven models. The traffic simulation uses newly reported incident information and the estimated origin-destination (OD) demand flows to capture the complex interaction between drivers and road network, and predicts traffic states under extreme conditions. We showcase the capability of the proposed data-driven enforced traffic simulation platform for incident impact analysis in a real-life sub-network in Sydney, Australia.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/152766