Nonlinearity in time-dependent origin-destination demand estimation in congested networks

Sajjad Shafiei, Meead Saberi, Hai L. Vu

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

1 Citation (Scopus)


Time-dependent origin-destination (TDOD) demand estimation is often formulated as a bi-level quadratic optimization in which the estimated demand in the upper-level problem is evaluated iteratively through a dynamic traffic assignment (DTA) model in the lower level. When congestion forms and propagates in the network, traditional solutions assuming a linear relation between demand flow and link flow become inaccurate and yield biased solutions. In this study, we study a sensitivity-based method taking into account the impact of other OD flows on the links' traffic volumes and densities. Thereafter, we compare the performance of the proposed method with several well-established solution methods for TDOD demand estimation problem. The methods are applied to a benchmark study urban network and a major freeway corridor in Melbourne, Australia. We show that the incorporation of traffic density into flow-based models improves the accuracy of the estimated OD flows and assist solution algorithm in avoiding converging to a sub-optimal result. Moreover, the final results obtained from the proposed sensitivity-based method contains less amount of error while the method exceeds the problem's computational intensity compared to the traditional linear method.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Transportation Systems Conference (ITSC)
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538670248
Publication statusPublished - 2019
EventIEEE Conference on Intelligent Transportation Systems 2019 - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019
Conference number: 22nd (Proceedings)


ConferenceIEEE Conference on Intelligent Transportation Systems 2019
Abbreviated titleITSC 2019
Country/TerritoryNew Zealand
Internet address

Cite this