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

Sajjad Shafiei; Meead Saberi Kalaee; Majid Sarvi

doi:10.1109/ITSC.2016.7795870

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

Sajjad Shafiei, Meead Saberi Kalaee, Majid Sarvi

Civil Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

3 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2016 IEEE 19^th International Conference on Intelligent Transportation Systems (ITSC 2016)
Subtitle of host publication	Rio de Janeiro, Brazil, November 1-4, 2016 [Proceedings]
Editors	Denis Wolf
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1945-1950
Number of pages	6
ISBN (Electronic)	9781509018895
ISBN (Print)	9781509018901
DOIs	https://doi.org/10.1109/ITSC.2016.7795870
Publication status	Published - 22 Dec 2016
Event	IEEE Conference on Intelligent Transportation Systems 2016 - Sheraton Rio Hotel & Resort, Rio de Janeiro, Brazil Duration: 1 Nov 2016 → 4 Nov 2016 Conference number: 19th https://ieeexplore.ieee.org/xpl/conhome/7784515/proceeding (Proceedings) https://web.fe.up.pt/~ieeeitsc2016/index.html (Website)

Conference

Conference	IEEE Conference on Intelligent Transportation Systems 2016
Abbreviated title	ITSC 2016
Country	Brazil
City	Rio de Janeiro
Period	1/11/16 → 4/11/16
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7784515/proceeding (Proceedings) https://web.fe.up.pt/~ieeeitsc2016/index.html (Website)

Access to Document

10.1109/ITSC.2016.7795870

Link to publication in Scopus

Cite this

Shafiei, S., Saberi Kalaee, M., & Sarvi, M. (2016). Application of an exact gradient method to estimate dynamic origin-destination demand for Melbourne network. In D. Wolf (Ed.), 2016 IEEE 19^th International Conference on Intelligent Transportation Systems (ITSC 2016): Rio de Janeiro, Brazil, November 1-4, 2016 [Proceedings] (pp. 1945-1950). [7795870] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITSC.2016.7795870

Shafiei, Sajjad ; Saberi Kalaee, Meead ; Sarvi, Majid. / Application of an exact gradient method to estimate dynamic origin-destination demand for Melbourne network. 2016 IEEE 19^th International Conference on Intelligent Transportation Systems (ITSC 2016): Rio de Janeiro, Brazil, November 1-4, 2016 [Proceedings]. editor / Denis Wolf. IEEE, Institute of Electrical and Electronics Engineers, 2016. pp. 1945-1950

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abstract = "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.",

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note = "IEEE Conference on Intelligent Transportation Systems 2016, ITSC 2016 ; Conference date: 01-11-2016 Through 04-11-2016",

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Shafiei, S, Saberi Kalaee, M & Sarvi, M 2016, Application of an exact gradient method to estimate dynamic origin-destination demand for Melbourne network. in D Wolf (ed.), 2016 IEEE 19^th International Conference on Intelligent Transportation Systems (ITSC 2016): Rio de Janeiro, Brazil, November 1-4, 2016 [Proceedings]., 7795870, IEEE, Institute of Electrical and Electronics Engineers, pp. 1945-1950, IEEE Conference on Intelligent Transportation Systems 2016, Rio de Janeiro, Brazil, 1/11/16. https://doi.org/10.1109/ITSC.2016.7795870

Application of an exact gradient method to estimate dynamic origin-destination demand for Melbourne network. / Shafiei, Sajjad; Saberi Kalaee, Meead; Sarvi, Majid.

2016 IEEE 19^th International Conference on Intelligent Transportation Systems (ITSC 2016): Rio de Janeiro, Brazil, November 1-4, 2016 [Proceedings]. ed. / Denis Wolf. IEEE, Institute of Electrical and Electronics Engineers, 2016. p. 1945-1950 7795870.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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Shafiei S, Saberi Kalaee M, Sarvi M. Application of an exact gradient method to estimate dynamic origin-destination demand for Melbourne network. In Wolf D, editor, 2016 IEEE 19^th International Conference on Intelligent Transportation Systems (ITSC 2016): Rio de Janeiro, Brazil, November 1-4, 2016 [Proceedings]. IEEE, Institute of Electrical and Electronics Engineers. 2016. p. 1945-1950. 7795870 https://doi.org/10.1109/ITSC.2016.7795870