Application of an exact gradient method to estimate dynamic origin-destination demand for Melbourne network

Sajjad Shafiei, Meead Saberi Kalaee, Majid Sarvi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


The time-dependent origin-destination (TDOD) demand estimation problem aims at estimating dynamic demand that represents the observed traffic flow patterns in a transportation network. Errors in TDOD demand are often propagated into the network outputs causing unreliable planning and operational policies. In this study, a bi-level optimization problem is proposed where the upper level is an Ordinary Least-Squared (OLS) error minimization problem that minimizes the deviation between the estimated and observed traffic volumes from SCATS, while the lower level generates assignment proportions matrix using a mesoscopic simulation-based dynamic user equilibrium model. The interior point conjugate gradient method, as an exact gradient method, is applied to solve the TDOD demand estimation problem. The obtained results highlight the capability of the proposed approach in improving the performance of a dynamic large-scale network model of Melbourne CBD.

Original languageEnglish
Title of host publication2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016)
Subtitle of host publicationRio de Janeiro, Brazil, November 1-4, 2016 [Proceedings]
EditorsDenis Wolf
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781509018895
ISBN (Print)9781509018901
Publication statusPublished - 22 Dec 2016
EventIEEE Conference on Intelligent Transportation Systems 2016 - Sheraton Rio Hotel & Resort, Rio de Janeiro, Brazil
Duration: 1 Nov 20164 Nov 2016
Conference number: 19th (Proceedings) (Website)


ConferenceIEEE Conference on Intelligent Transportation Systems 2016
Abbreviated titleITSC 2016
CityRio de Janeiro
Internet address

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