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 |
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Title of host publication | 2016 IEEE 19th 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 | |
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 |
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Abbreviated title | ITSC 2016 |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 1/11/16 → 4/11/16 |
Internet address |