Estimating time-dependent origin–destination demand from traffic counts: extended gradient method

Abstract

Time-dependent origin–destination (TDOD) demand is a key input of dynamic traffic assignment (DTA) in advanced traffic management systems. Model reliability is highly dependent on the accuracy of this information. One method to achieve TDOD demand matrices is to use a primary demand matrix and traffic volume counts in some links of a network. This paper proposes a bi-level model to correct the TDOD demand matrix. The extended gradient method (EGM) – an iterative method that minimizes the discrepancy between the counted and estimated traffic volumes – is a suggested means to solve this problem. The methodology is first tested on a small synthetic network to verify its performance. Then, it is applied to a real network to demonstrate its scalability. The results illustrate the effectiveness of this algorithm for the correction of TDOD demand matrices.

Keywords:

• Time-dependent origin–destination demand estimation
• Dynamic traffic assignment
• Extended gradient method

Acknowledgement

The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The authors gratefully acknowledge the contributions of PTV group for providing the VISUM software academic license for this study.